Complete content from labelf.ai # Blog > Articles about AI, machine learning, NLP, and real-world use cases from the Labelf team. Insights on AI, machine learning, and how to put them to work. [](/blog/treat-people-as-people-at-scale) [AI](/blog/treat-people-as-people-at-scale) ## [We Treat People as People, at Scale](/blog/treat-people-as-people-at-scale) [One-to-one marketing was promised in the 90s and never delivered. Now AI makes it possible — not by automating away the human interaction, but by making every interaction radically smarter.](/blog/treat-people-as-people-at-scale) [T](/blog/treat-people-as-people-at-scale) [The Labelf Team](/blog/treat-people-as-people-at-scale) [· April 8, 2026](/blog/treat-people-as-people-at-scale) [Read more →](/blog/treat-people-as-people-at-scale) [](/blog/youre-renting-your-customers) [AI](/blog/youre-renting-your-customers) ### [You're Renting Your Customers — And The Price Goes Up Every Year](/blog/youre-renting-your-customers) [While everyone celebrates the shift to self-service and automation, companies are quietly paying more and more to reach the customers they already had. There is another way.](/blog/youre-renting-your-customers) [T](/blog/youre-renting-your-customers) [The Labelf Team · April 2, 2026](/blog/youre-renting-your-customers) [](/blog/every-team-needs-a-different-dashboard) [Use cases](/blog/every-team-needs-a-different-dashboard) ### [Every Team Needs a Different Dashboard — Here's How to Build Yours](/blog/every-team-needs-a-different-dashboard) [Your Head of Ops needs cost data. Your team leads need agent scores. Your process developers need root cause alerts. One dashboard can't serve them all. Dashboard Studio can.](/blog/every-team-needs-a-different-dashboard) [T](/blog/every-team-needs-a-different-dashboard) [The Labelf Team · June 9, 2023](/blog/every-team-needs-a-different-dashboard) [](/blog/nlp-techniques) [AI](/blog/nlp-techniques) ### [NLP Techniques](/blog/nlp-techniques) [An exploration of the most important natural language processing techniques used in modern AI, from tokenization and word embeddings to transformers and large language models.](/blog/nlp-techniques) [F](/blog/nlp-techniques) [Filip Sörlin · November 21, 2022](/blog/nlp-techniques) [](/blog/what-is-accuracy-precision-recall-and-f1-score) [AI](/blog/what-is-accuracy-precision-recall-and-f1-score) ### [What Is Accuracy, Precision, Recall, and F1 Score?](/blog/what-is-accuracy-precision-recall-and-f1-score) [A clear guide to the most common classification metrics in machine learning: accuracy, precision, recall, and F1 score. Learn when to use each metric and how they relate to real-world model performance.](/blog/what-is-accuracy-precision-recall-and-f1-score) [T](/blog/what-is-accuracy-precision-recall-and-f1-score) [Ted Tigerschiold · November 7, 2022](/blog/what-is-accuracy-precision-recall-and-f1-score) [](/blog/10-ways-ai-turns-customer-conversations-into-business-results) [AI](/blog/10-ways-ai-turns-customer-conversations-into-business-results) ### [10 Ways AI Turns Customer Conversations Into Business Results](/blog/10-ways-ai-turns-customer-conversations-into-business-results) [Every customer interaction contains signals — churn risk, sales opportunities, broken processes, coaching gaps. Here are ten proven ways to extract them and act on them, today.](/blog/10-ways-ai-turns-customer-conversations-into-business-results) [T](/blog/10-ways-ai-turns-customer-conversations-into-business-results) [The Labelf Team · October 25, 2022](/blog/10-ways-ai-turns-customer-conversations-into-business-results) [](/blog/four-seasons-of-ai-the-brief-history-of-artificial-intelligence-i) [AI](/blog/four-seasons-of-ai-the-brief-history-of-artificial-intelligence-i) ### [Four Seasons of AI: A Brief History](/blog/four-seasons-of-ai-the-brief-history-of-artificial-intelligence-i) [A journey through the history of artificial intelligence, from its optimistic beginnings in the 1950s through AI winters and resurgences, to the modern era of deep learning and large language models.](/blog/four-seasons-of-ai-the-brief-history-of-artificial-intelligence-i) [A](/blog/four-seasons-of-ai-the-brief-history-of-artificial-intelligence-i) [Antony Lu · August 26, 2022](/blog/four-seasons-of-ai-the-brief-history-of-artificial-intelligence-i) [](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [Use cases](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) ### [Voice of the Customer: AI-Powered Text Analytics](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [Discover how Voice of the Customer (VoC) analytics powered by AI can help businesses understand customer sentiment, identify churn drivers, and make data-driven decisions from text feedback.](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [A](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [Antony Lu · August 26, 2022](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [Use cases](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) ### [AI-Powered Ticket Routing for Customer Support](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [Learn how AI-powered automated ticket routing can improve response times, accuracy, and operational efficiency in customer support, with context-based analysis that understands multiple languages.](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [A](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [Antony Lu · July 6, 2022](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [](/blog/how-does-deep-learning-work) [AI](/blog/how-does-deep-learning-work) ### [How Does Deep Learning Work?](/blog/how-does-deep-learning-work) [A comprehensive overview of deep learning, exploring how neural networks learn from data, the architecture behind modern AI systems, and why deep learning has become the backbone of artificial intelligence.](/blog/how-does-deep-learning-work) [P](/blog/how-does-deep-learning-work) [Per Näslund · January 11, 2021](/blog/how-does-deep-learning-work) No posts in this category yet. --- # 10 Ways AI Turns Customer Conversations Into Business Results > Every customer interaction contains signals — churn risk, sales opportunities, broken processes, coaching gaps. Here are ten proven ways to extract them and act on them, today. [ Back to blog](/blog)  *** Your customers tell you everything — in every call, every chat, every email. They tell you what’s broken, what they want, why they’re leaving, and what would make them stay. The problem is that no human can read 50,000 conversations a month and connect the dots. AI can. And it doesn’t just read them — it classifies, quantifies, and turns them into actions with dollar signs attached. Here are ten ways companies are doing this right now, each one grounded in real operational outcomes. No theory. No hype. Just what works. *** ## 1. Predict and Prevent Churn By the time a customer calls to cancel, the decision was made weeks ago. The warning signs were in earlier conversations — a tone shift, a competitor mention, a repeated issue that never got resolved. AI models trained on your conversation data find these patterns and flag at-risk customers before they reach the cancellation page. But prediction alone is worthless. What matters is the response. That’s why the best systems don’t just flag risk — they generate an individualized recovery plan for each customer: what went wrong, what to say, what to offer, and what’s worked for similar customers in the past. The numbers: organizations that deploy churn prediction on conversation data routinely identify **67% of preventable churn** in advance. And since it costs 5x more to acquire a customer than to retain one, the math is overwhelming. [See how churn reduction works →](/solutions/churn-reduction) *** ## 2. Find Sales Signals Hiding in Support Calls Companies spend millions on cold outreach while ignoring the richest source of sales signals — the conversations already happening with existing customers every day. A customer mentions their summer house? That’s a broadband upgrade. They stream sports? That’s a TV pitch. They ask about a feature they don’t have? That’s a natural upsell. AI reads every interaction and builds a living profile of each customer — interests, product gaps, timing signals, campaign fit. Each lead comes with context and a personalized pitch grounded in what the customer actually cares about. Not “Dear valued customer” — “Never miss a game again.” The companies doing this find their support function contributes meaningfully to revenue growth. Cost center becomes strategic asset. [See how sales intelligence works →](/solutions/sales-opportunities) *** ## 3. Understand Why Customers Contact You Before you can fix anything, you need to know what’s happening and why. AI classifies every interaction into your categories — billing, technical, cancellation, sales — automatically, across all channels, in any language. But classification alone is table stakes. The real value is in the second layer: **root cause analysis**. Not just “billing inquiry” but *why* — system error, unclear invoice format, price change confusion. Each reason gets a volume count and a cost attached. You define the categories. The AI does the rest. Train models in your language, your lingo, your logic — no data science required. And if you don’t know what categories to start with, auto-categorization reads everything and discovers them for you. [See how contact reasons work →](/solutions/contact-reasons) *** ## 4. Detect Broken Processes Before They Cost You Your agents aren’t the problem. Broken processes are. App bugs, IVR misdirections, system errors — they generate thousands of unnecessary contacts. Every one costs money. Most go undetected for months. AI finds them by reading what customers describe: “the app crashed when I tried to change my address” is a bug report hiding in a support ticket. Cluster enough of these and you get a prioritized list of process issues, each with a monthly cost attached. Bug reports with price tags, routed to the team who can fix them. One telecom discovered that a single firmware bug was generating 1,200 calls per month at $12 per call — $14,400/month in pure waste. The fix took two days. They’d been paying for it for six months. [See how process improvement works →](/solutions/process-improvement) *** ## 5. Coach Agents Based on Evidence, Not Guesswork  Every contact center has top performers — agents who consistently resolve issues faster and generate higher satisfaction scores. The gap between them and everyone else is where customer satisfaction lives or dies. Manual QA catches 2% of interactions. You create playbooks but have no idea if anyone follows them. AI changes both. First, it analyzes thousands of conversations to identify what top performers actually do differently — specific phrases, techniques, sequences. These become data-backed playbooks with real conversation citations. Then, custom ML models detect whether agents follow the playbook. Track adherence rates automatically. Measure the cost of non-adherence in dollars. When a coach sits down with an agent, they bring evidence: “Here’s what Sarah does on billing disputes that you skip. Her CSAT on these is 4.6. Yours is 3.1. Let me show you the conversations.” [See how agent coaching works →](/solutions/agent-coaching) *** ## 6. Monitor AI Agent Quality in Real Time You’re deploying AI chatbots and voice agents. But who watches them? They hallucinate, go off-script, and break compliance rules — and you won’t know until a customer complains. AI monitoring scores every bot interaction on accuracy, tone, compliance, and resolution quality. Violations are flagged instantly. Bad agents get replaced. Good ones get promoted. Every decision is logged for regulators. And it’s not just AI agents — the same system monitors human agent quality too. Same scoring, same compliance checks, same audit trails. Compare human vs AI performance side by side. See which handles which categories better. Route accordingly. With EU AI Act requirements tightening, this isn’t optional anymore. It’s the cost of deploying AI responsibly. [See how quality monitoring works →](/solutions/quality-assurance) *** ## 7. Map What Delights and What Frustrates  A CSAT score tells you the temperature. It doesn’t tell you why the patient is sick. AI connects satisfaction to specific interactions, agent behaviors, and patterns. You don’t just see the score — you see what’s driving it up and what’s pulling it down. Which agent behaviors create promoters? Which touchpoints create friction? What should you do more of? The output is a delight-and-friction map: ranked lists of what creates happy customers (first-call resolution, proactive callbacks, remembered context) and what destroys trust (unnecessary transfers, repeated explanations, broken promises). Each item quantified by impact and volume. Promoters have 3.2x the lifetime value of detractors. This is where you move that needle. [See how customer experience works →](/solutions/customer-experience) *** ## 8. Quantify Operational Waste in Dollars Knowing why customers call is step one. Knowing what it costs is where savings happen. AI gives every contact reason a cost breakdown. Every waste source gets a price tag. Every improvement gets measured in dollars saved — not “AHT is up” but **“AHT being 42 seconds above target costs $14,000 per month.”** The system tracks 13+ KPIs with targets and cost-of-gap: handle time, resolution rate, transfer rate, first contact resolution, CSAT, wait time, cost per contact. When a KPI misses its target, you see what it costs. When you fix it, you prove what you saved. Average waste identified: 23% of total contact center spend. That’s real money returned to the business. [See how operational efficiency works →](/solutions/operational-efficiency) *** ## 9. Search by Meaning, Not Keywords When a customer says “I’ve had enough,” they mean cancellation — but keyword search for “cancellation” won’t find it. Semantic search understands intent. Type a question, a description, or even a feeling. AI finds matching conversations across millions of interactions — even when the words are completely different. Cross-language too: search in English, find results in Swedish, Norwegian, or German. This is how you find the 200 fraud cases hiding in 5 million conversations. How you discover that 1,400 customers mentioned a competitor’s new pricing before marketing even knew about the campaign. How a product team confirms a bug exists across customer segments in 30 seconds instead of three weeks. [See how AI search works →](/platform/ai-search) *** ## 10. Ask Any Question and Get an Answer You have a question about your customers. Why are they leaving? What do your best agents do differently? Where does your process break? Which campaign converts best for this segment? An AI agent with access to every model, every metric, and every conversation answers in seconds — with charts, citations, and recommended actions. It writes SQL, reads transcripts, builds visualizations, and suggests next steps. All from a single question in plain language. This is what democratized business intelligence looks like. Product teams see top customer pain points in real time. Marketing tracks how messaging changes affect sentiment. Operations measures the impact of process changes on contact volume. Every department accesses the data relevant to their work, without waiting for quarterly reports or analyst availability. [See how the AI agent works →](/platform/ai-agent) *** ## Where to Start  These ten use cases share a common thread: they all start with the conversations you are already having. You don’t need new data sources, expensive integrations, or a team of machine learning engineers. Start with the problem that costs you the most. Prove the value in 30 days. Expand from there. The conversations are happening right now. The only question is whether you’re listening. Share this article Copy link [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Flabelf.ai%2Fblog%2F10-ways-ai-turns-customer-conversations-into-business-results "Share on LinkedIn") [](https://twitter.com/intent/tweet?url=https%3A%2F%2Flabelf.ai%2Fblog%2F10-ways-ai-turns-customer-conversations-into-business-results\&text=10%20Ways%20AI%20Turns%20Customer%20Conversations%20Into%20Business%20Results "Share on X")  The Labelf Team Team ## More articles [](/blog/treat-people-as-people-at-scale) [AI](/blog/treat-people-as-people-at-scale) ### [We Treat People as People, at Scale](/blog/treat-people-as-people-at-scale) [One-to-one marketing was promised in the 90s and never delivered. Now AI makes it possible — not by automating away the human interaction, but by making every interaction radically smarter.](/blog/treat-people-as-people-at-scale) [T](/blog/treat-people-as-people-at-scale) [The Labelf Team · April 8, 2026](/blog/treat-people-as-people-at-scale) [](/blog/youre-renting-your-customers) [AI](/blog/youre-renting-your-customers) ### [You're Renting Your Customers — And The Price Goes Up Every Year](/blog/youre-renting-your-customers) [While everyone celebrates the shift to self-service and automation, companies are quietly paying more and more to reach the customers they already had. There is another way.](/blog/youre-renting-your-customers) [T](/blog/youre-renting-your-customers) [The Labelf Team · April 2, 2026](/blog/youre-renting-your-customers) [](/blog/nlp-techniques) [AI](/blog/nlp-techniques) ### [NLP Techniques](/blog/nlp-techniques) [An exploration of the most important natural language processing techniques used in modern AI, from tokenization and word embeddings to transformers and large language models.](/blog/nlp-techniques) [F](/blog/nlp-techniques) [Filip Sörlin · November 21, 2022](/blog/nlp-techniques) --- # AI-Powered Ticket Routing for Customer Support > Learn how AI-powered automated ticket routing can improve response times, accuracy, and operational efficiency in customer support, with context-based analysis that understands multiple languages. [ Back to blog](/blog)  *** Customer support is no longer a back-office function you can afford to neglect. It is a competitive differentiator — one that directly influences whether customers stay, spend more, or recommend you to others. According to Qualtrics (2020), customers who rate a company’s service as “good” are 38% more likely to recommend that company to someone else. Salesforce found that 89% of consumers are more likely to make another purchase after a positive customer service experience. These are not marginal effects. They represent the difference between a business that grows through advocacy and one that bleeds customers quietly. The challenge is that delivering consistently great support at scale is extraordinarily difficult. As ticket volumes increase, teams face mounting pressure to respond faster, route inquiries to the right specialists, and maintain quality across every interaction. Manual processes that worked when you had fifty tickets a day collapse under the weight of five hundred or five thousand. This is where automated ticket routing enters the picture — not as a nice-to-have optimization, but as a foundational capability that determines whether your support operation can scale without sacrificing the experience your customers expect.  ## The Problems With Manual Ticket Routing Manual ticket routing relies on human judgment at every step. An agent reads an incoming request, interprets its intent, and assigns it to what they believe is the correct queue or specialist. This works reasonably well in small teams where everyone knows everyone else’s expertise. But as organizations grow, the system breaks down in predictable and costly ways. ### Speed Is Non-Negotiable The most immediate problem is speed. Customer expectations around response time have shifted dramatically in recent years, and there is very little tolerance for delays. According to a 2018 HubSpot study, 90% of customers rate an immediate response as essential or very important when they have a customer service question. Even more striking, 60% of customers define “immediate” as ten minutes or less. That is not a lot of time to receive a ticket, read it, understand the context, determine which team should handle it, and route it — especially when the person doing the routing is also handling their own queue of active conversations. The consequences of slow routing extend beyond individual interactions. Research shows that only 17% of customers would recommend a brand that delivers a slow but ultimately effective solution. In other words, getting the answer right is not enough if it takes too long to deliver. Speed and quality are not separate dimensions of service — customers evaluate them together, and falling short on either one damages the relationship. When tickets sit in the wrong queue waiting to be rerouted, every minute adds friction. The customer waits longer, the agent who eventually handles the ticket has less context, and the entire chain of handoffs creates opportunities for information to be lost or misunderstood. In high-volume environments, these delays compound across hundreds of tickets per day, creating systemic bottlenecks that no amount of staffing can resolve. ### Accuracy Discrepancies Across AI Approaches Not all automation is created equal. Many organizations have experimented with basic rule-based routing or keyword-matching systems, only to find that accuracy falls short of what is needed for reliable operation. A ticket containing the word “billing” might seem straightforward to route, but what if the customer is actually asking about a billing error caused by a technical bug? Keyword-based systems struggle with ambiguity, sarcasm, multi-topic tickets, and the countless ways customers express the same underlying issue using different language. More sophisticated AI approaches — such as traditional machine learning classifiers — improve on keyword matching but still require significant volumes of labeled training data to reach acceptable accuracy levels. Building and maintaining those training datasets is expensive and time-consuming, and the models can degrade quickly as products evolve, new issue types emerge, or customer language shifts. The gap between the accuracy a routing system achieves in testing and the accuracy it delivers in production is often much wider than teams expect. This accuracy gap has real operational consequences. Every misrouted ticket triggers a chain of inefficiency: the wrong agent spends time reading a ticket they cannot resolve, the ticket gets reassigned, the customer waits longer, and the agent who ultimately handles it has to start from scratch. At scale, even a small percentage of misrouted tickets creates a meaningful drag on team productivity and customer satisfaction. ### Multi-Language Challenges For organizations that serve customers in multiple languages, manual routing becomes even more complex. Agents need to identify the language of an incoming ticket, assess its content, and route it to a specialist who both speaks that language and has the relevant domain expertise. This is a difficult matching problem even for experienced humans. Training traditional AI models to handle multiple languages compounds the challenge further. Each language typically requires its own training data, its own labeled examples, and its own validation process. For a company operating in ten or fifteen languages, this means maintaining ten or fifteen separate datasets — each of which needs to be updated as the product and customer base evolve. The cost and complexity quickly become impractical, particularly for mid-size organizations that lack dedicated machine learning teams. The result is that many multilingual support operations fall back on manual routing for non-primary languages, which reintroduces all the speed and accuracy problems described above — often in markets where the company can least afford to deliver a subpar experience. ## Values and Opportunities of Automated Ticket Routing The case for automated ticket routing is not about replacing human judgment entirely. It is about removing the bottleneck that human-dependent routing creates and freeing your agents to focus on what they do best: solving problems and building relationships with customers.  ### Context-Based Analysis That Actually Understands Meaning The breakthrough that makes modern automated ticket routing viable is context-based analysis. Rather than matching keywords or relying on rigid rules, advanced natural language processing models analyze the relationships between words in a ticket to understand what the customer is actually asking about. This means the system can distinguish between “I want to cancel my subscription” and “I want to know if I can pause my subscription instead of canceling” — two tickets that contain similar words but require entirely different handling. Labelf takes this approach and makes it accessible without requiring machine learning expertise. By leveraging contextual language models, Labelf can analyze the meaning behind customer messages and route them with high accuracy. Organizations using this approach have achieved 90% routing accuracy in less than an hour of setup time — a result that would take weeks or months to reach with traditional machine learning workflows that require extensive data labeling and model training. This context-based approach also handles the multi-language challenge naturally. Because modern language models understand semantic relationships across languages, a single model can process tickets in English, Swedish, Spanish, German, or any other supported language without requiring separate training datasets for each. The system understands that a customer complaint written in French carries the same urgency and intent as the equivalent message in English, and routes both appropriately. ### Higher Speed and Operational Efficiency Automated routing eliminates the single biggest source of delay in the support workflow: the time between when a ticket arrives and when the right agent starts working on it. Instead of sitting in a general queue waiting for a human dispatcher to read, categorize, and assign it, every incoming ticket is analyzed and routed in seconds. The impact on response times is immediate and significant. But the second-order effects are equally important. When tickets consistently reach the right agent on the first attempt, the need for internal transfers and rerouting drops dramatically. Agents spend less time on tickets outside their expertise, which means they resolve issues faster and with higher confidence. The overall throughput of the support team increases without adding headcount. This efficiency gain compounds over time. As the routing system processes more tickets, it generates data that can be used to identify emerging issue types, spot trends in customer behavior, and optimize team structure. The routing layer becomes not just a workflow tool but an intelligence layer that continuously improves the operation it supports. ### Customization That Fits Your Operation Every support organization is different, and a routing system that forces you into a one-size-fits-all framework will always leave gaps. Effective automated routing needs to accommodate the specific way your team is organized and the specific priorities that drive your business. With Labelf, you can configure routing logic based on the dimensions that matter most to your operation. Route tickets by query type — separating technical issues from billing questions from feature requests — so that each category reaches the team with the deepest relevant expertise. Route by customer journey stage, ensuring that onboarding questions go to specialists who understand the new-customer experience while retention-related inquiries reach agents trained in save techniques. Route by urgency level, so that time-sensitive issues like service outages or payment failures are escalated immediately rather than waiting their turn in a general queue. This flexibility means the routing system adapts to your operation rather than the other way around. As your team evolves, new products launch, or customer needs shift, you can adjust the routing logic without rebuilding the underlying model. The system grows with you instead of becoming a constraint you have to work around. ## Conclusion Automated ticket routing is not a futuristic ambition — it is a practical capability that organizations can deploy today to address some of the most persistent challenges in customer support. The combination of slow manual routing, accuracy limitations in legacy automation, and the complexity of multilingual operations creates a compounding drag on support quality that no amount of incremental process improvement can fully resolve. Context-based AI routing changes the equation. By understanding the meaning behind customer messages rather than just matching keywords, it delivers the accuracy needed for reliable operation. By processing tickets in seconds rather than minutes, it meets the speed expectations that customers now consider non-negotiable. And by handling multiple languages through a single model, it removes one of the most significant barriers to delivering consistent global support. The organizations that invest in this capability now will not just reduce costs and improve metrics — they will build the kind of support experience that turns customers into advocates. In a market where 89% of customers are more likely to buy again after a positive service interaction, that is a competitive advantage worth pursuing. Share this article Copy link [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fdeploy-automated-ticket-routing-for-exceptional-customer-support "Share on LinkedIn") [](https://twitter.com/intent/tweet?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fdeploy-automated-ticket-routing-for-exceptional-customer-support\&text=AI-Powered%20Ticket%20Routing%20for%20Customer%20Support "Share on X")  Antony Lu Contributor ## More articles [](/blog/every-team-needs-a-different-dashboard) [Use cases](/blog/every-team-needs-a-different-dashboard) ### [Every Team Needs a Different Dashboard — Here's How to Build Yours](/blog/every-team-needs-a-different-dashboard) [Your Head of Ops needs cost data. Your team leads need agent scores. Your process developers need root cause alerts. One dashboard can't serve them all. Dashboard Studio can.](/blog/every-team-needs-a-different-dashboard) [T](/blog/every-team-needs-a-different-dashboard) [The Labelf Team · June 9, 2023](/blog/every-team-needs-a-different-dashboard) [](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [Use cases](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) ### [Voice of the Customer: AI-Powered Text Analytics](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [Discover how Voice of the Customer (VoC) analytics powered by AI can help businesses understand customer sentiment, identify churn drivers, and make data-driven decisions from text feedback.](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [A](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [Antony Lu · August 26, 2022](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [](/blog/treat-people-as-people-at-scale) [AI](/blog/treat-people-as-people-at-scale) ### [We Treat People as People, at Scale](/blog/treat-people-as-people-at-scale) [One-to-one marketing was promised in the 90s and never delivered. Now AI makes it possible — not by automating away the human interaction, but by making every interaction radically smarter.](/blog/treat-people-as-people-at-scale) [T](/blog/treat-people-as-people-at-scale) [The Labelf Team · April 8, 2026](/blog/treat-people-as-people-at-scale) --- # Every Team Needs a Different Dashboard — Here's How to Build Yours > Your Head of Ops needs cost data. Your team leads need agent scores. Your process developers need root cause alerts. One dashboard can't serve them all. Dashboard Studio can. [ Back to blog](/blog)  *** Every role in a customer operation has a different question they wake up to. The Head of Operations wants to know what’s costing money. The team lead wants to know who needs coaching. The process developer wants to know which system bugs are generating the most calls. The CX manager wants to know what’s driving CSAT down. They all need dashboards. But they don’t need the *same* dashboard. This is the fundamental problem with most analytics tools: they give you one view and expect everyone to use it. The result is that half the organization ignores the data because it doesn’t answer their specific question. Or worse — they export it to Excel and build their own version, which is outdated by the time they finish. Dashboard Studio solves this by letting every stakeholder build exactly the view they need — from templates or from scratch. *** ## Start With Templates, Customize Everything You don’t start from a blank canvas unless you want to. Labelf ships with pre-built dashboard templates for the most common operational views: * **Churn Dashboard** — at-risk customers ranked by revenue, with recovery actions and save rates * **Sales Dashboard** — upsell opportunities surfaced from conversations, conversion tracking by campaign * **Contact Reasons** — why customers call, what it costs, how it’s trending * **Agent Performance** — quality scores, playbook adherence, coaching priorities * **Process Alerts** — broken flows and system bugs ranked by monthly cost Each template is a starting point. Change the metrics, add charts, adjust filters, swap time ranges. Every dimension in your data is a filter. Every metric has a target. Every alert has a cost. *** ## What Each Role Actually Needs ### Head of Operations: The Money View The ops leader doesn’t need to see individual conversations. They need to see where money is being wasted and where it’s being saved. Their dashboard shows: total contact volume, cost per contact by category, waste breakdown (unnecessary transfers, repeat contacts, non-resolution), and savings tracking over time. Every number has a dollar sign. Every trend line answers the question: are we getting better or worse? The most powerful widget: **cost-of-gap**. When AHT is 42 seconds above target, the dashboard doesn’t say “AHT is high.” It says “AHT gap costs $14,000/month.” That changes the conversation from “we should improve” to “we’re losing this much money every month we don’t.” [See operational efficiency →](/solutions/operational-efficiency) *** ### Team Lead: The People View Team leads need agent-level data — but not raw numbers. They need coaching intelligence. Their dashboard shows: agent quality scores benchmarked against the team, playbook adherence rates per agent, CSAT by agent broken down by contact category, and a coaching priority list ranked by impact. The key insight: it’s not about finding who’s “bad.” It’s about finding the **specific gap** for each agent. Agent A might be excellent at billing but weak on technical. Agent B might have great empathy scores but slow resolution. The dashboard surfaces exactly what each person needs to work on, with conversation citations to use in 1:1 coaching. [See agent coaching →](/solutions/agent-coaching) *** ### Process Developer: The System View Process developers don’t care about individual agents. They care about broken systems that generate unnecessary contacts. Their dashboard shows: process alerts ranked by cost, root cause clusters, deflection opportunities (contacts that shouldn’t exist), and trend monitoring for anomalies. This is where conversations become bug reports. “The app crashed when I tried to change my address” × 1,200 calls/month × $12/call = $14,400/month. That’s not a support problem. That’s an engineering ticket with a price tag attached. [See process improvement →](/solutions/process-improvement) *** ### CX Manager: The Relationship View CX managers need to connect satisfaction to specific drivers — not just track a number. Their dashboard shows: CSAT by contact reason, delight drivers vs friction points, promoter-to-detractor conversion rates, and relationship trajectory over time. The question it answers: what should we do more of, and what should we stop doing? The most valuable chart: the delight-and-friction map. A ranked list of what creates happy customers (first-call resolution, proactive callbacks, remembered context) and what destroys trust (unnecessary transfers, repeated explanations, broken promises). Each item quantified by CSAT impact and volume. [See customer experience →](/solutions/customer-experience) *** ## Build From Scratch: 25+ Chart Types When templates aren’t enough, Studio gives you the full toolkit: * **KPI cards** with targets and trend sparklines * **Time series** for tracking any metric over time * **Bar charts** for comparing categories, teams, agents * **Pie and treemap** for volume distribution * **Tables** with sorting, filtering, and drill-down * **Gauge charts** for target tracking * **Radar charts** for multi-dimensional agent comparison * **Funnel charts** for conversion flows Every chart supports filters, date ranges, and cross-referencing. Click a bar to drill into the conversations behind it. Every number connects back to the real customer interactions that generated it. *** ## Real-Time, Not Last Quarter The fundamental shift: these dashboards update continuously. Not weekly. Not monthly. Not “when someone remembers to run the report.” When a software update ships and calls spike about a specific feature, you see it within hours — not when someone reviews last month’s numbers. When an agent’s quality drops, you catch it in days — not at the next quarterly review. This is what makes the difference between reactive firefighting and proactive operations. You can push a customer communication about a known issue before the volume spirals. You can coach an agent before a pattern becomes a habit. You can fix a process before it costs you another $50,000. *** ## Getting Started Every Labelf deployment includes Dashboard Studio. The standard templates are ready on day one. Custom dashboards typically take 30 minutes to build. The data is already flowing through your conversations. The only question is who needs to see it and what question they need answered. [See all platform capabilities →](/the-platform) Share this article Copy link [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fevery-team-needs-a-different-dashboard "Share on LinkedIn") [](https://twitter.com/intent/tweet?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fevery-team-needs-a-different-dashboard\&text=Every%20Team%20Needs%20a%20Different%20Dashboard%20%E2%80%94%20Here's%20How%20to%20Build%20Yours "Share on X")  The Labelf Team Team ## More articles [](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [Use cases](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) ### [Voice of the Customer: AI-Powered Text Analytics](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [Discover how Voice of the Customer (VoC) analytics powered by AI can help businesses understand customer sentiment, identify churn drivers, and make data-driven decisions from text feedback.](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [A](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [Antony Lu · August 26, 2022](/blog/understand-the-voice-of-the-customers-through-optimizing-text-analytics) [](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [Use cases](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) ### [AI-Powered Ticket Routing for Customer Support](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [Learn how AI-powered automated ticket routing can improve response times, accuracy, and operational efficiency in customer support, with context-based analysis that understands multiple languages.](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [A](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [Antony Lu · July 6, 2022](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [](/blog/treat-people-as-people-at-scale) [AI](/blog/treat-people-as-people-at-scale) ### [We Treat People as People, at Scale](/blog/treat-people-as-people-at-scale) [One-to-one marketing was promised in the 90s and never delivered. Now AI makes it possible — not by automating away the human interaction, but by making every interaction radically smarter.](/blog/treat-people-as-people-at-scale) [T](/blog/treat-people-as-people-at-scale) [The Labelf Team · April 8, 2026](/blog/treat-people-as-people-at-scale) --- # Four Seasons of AI: A Brief History > A journey through the history of artificial intelligence, from its optimistic beginnings in the 1950s through AI winters and resurgences, to the modern era of deep learning and large language models. [ Back to blog](/blog)  *** Few fields in science have experienced such dramatic swings between euphoria and despair as artificial intelligence. Since the mid-twentieth century, AI has moved through recurring cycles — bursts of optimism where researchers predicted human-level machines were just years away, followed by painful periods of disillusionment, funding cuts, and public skepticism. These phases have come to be known as “AI springs,” “AI summers,” and “AI winters.” Understanding them is not merely an exercise in nostalgia. The patterns of overpromise and correction that shaped the field’s past continue to inform how we develop, fund, and talk about AI today. This is the story of those seasons. ## AI Spring: The Birth of AI Long before the term “artificial intelligence” existed, thinkers on both sides of the Atlantic were laying its intellectual foundations — sometimes in fiction, sometimes in wartime secrecy, and sometimes with nothing more than pen and paper. ### The United States: Asimov and the Dream of Thinking Machines In 1942, Isaac Asimov published the short story *Runaround*, introducing his famous Three Laws of Robotics — a set of ethical rules governing the behavior of intelligent machines. Although pinpointing the exact roots of AI is difficult, Asimov’s fiction proved remarkably influential. His stories gave scientists and engineers a shared vocabulary for thinking about machine autonomy, moral reasoning, and the relationship between humans and their creations. Generations of researchers would later cite Asimov as the spark that first drew them toward the field. ### The United Kingdom: Alan Turing and the Foundations of Computing Across the Atlantic, the groundwork for AI was being laid in a far more urgent context. During the Second World War, the British mathematician Alan Turing developed “The Bombe,” an electromechanical device used to decipher messages encrypted by the German Enigma machine. Turing’s wartime work demonstrated that complex reasoning tasks could, in principle, be mechanized. In 1950, he published the landmark paper *Computing Machinery and Intelligence*, in which he posed the deceptively simple question: “Can machines think?” He proposed what is now known as the Turing Test — the idea that a machine could be considered intelligent if a human interrogator could not reliably distinguish its responses from those of another person. Turing is widely regarded as the father of both theoretical computer science and artificial intelligence.  ### Sweden: Arne Beurling and the Art of Codebreaking Sweden contributed its own quiet genius. The mathematician Arne Beurling managed to decipher the encrypted teleprinter traffic of Nazi Germany — in just two weeks, using nothing but pen and paper. His feat was one of the great intellectual achievements of the war, and it demonstrated the kind of pattern recognition and abstract reasoning that AI researchers would later try to replicate in machines. When colleagues asked Beurling how he had done it, he offered only a cryptic reply: “A magician does not reveal his secrets.” ### The Dartmouth Conference: AI Gets Its Name The field officially came into being in the summer of 1956, when a young mathematician named John McCarthy organized a workshop at Dartmouth College in Hanover, New Hampshire. Together with Marvin Minsky, Nathaniel Rochester, and Claude Shannon, McCarthy proposed studying the conjecture that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” It was in the proposal for this workshop that the term “Artificial Intelligence” was coined. The Dartmouth Conference brought together many of the researchers who would dominate the field for decades. It marked the moment AI ceased to be a scattered collection of ideas and became a recognized discipline with a name, an agenda, and a community. ## AI Summer and Winter ### The First Summer: Optimism and Early Breakthroughs The years following Dartmouth were electric with possibility. Funding flowed from government agencies, and researchers made rapid early progress. One notable achievement was ELIZA, a natural language processing program created by Joseph Weizenbaum at MIT in 1966. ELIZA simulated a Rogerian psychotherapist, reflecting users’ statements back at them as questions. It was remarkably simple in its design, yet people who interacted with it often became emotionally attached, confiding in the program as though it were a real therapist. ELIZA demonstrated — perhaps for the first time — that machines could create a convincing illusion of understanding, even without any actual comprehension.  The optimism of this era reached its peak when Marvin Minsky, by then one of the most prominent figures in AI, declared in 1970 that machines would achieve human-level intelligence “within three to eight years.” It was a bold claim, and it captured the mood of a field that believed its greatest challenges were essentially solved in principle, with only engineering details left to work out. ### The First Winter: Lighthill, Congressional Criticism, and Funding Cuts Reality intervened swiftly. In 1973, the British mathematician Sir James Lighthill published a devastating critique of AI research for the UK Science Research Council. Lighthill argued that the field had failed to deliver on its grand promises and that progress in AI was far more limited than its advocates admitted. His report led the British government to halt funding for AI research at all but a handful of universities. Across the Atlantic, similar skepticism was building. The United States Congress questioned the return on its AI investments, and funding from agencies like DARPA was sharply reduced. The first “AI winter” had arrived — a period defined not so much by a lack of ideas as by a painful gap between what researchers had promised and what they had actually delivered. ### The Second Summer: Expert Systems and Commercial Interest The chill did not last forever. In the early 1980s, a new approach called “expert systems” revived interest in AI. These programs encoded the knowledge of human specialists into large sets of if-then rules, enabling machines to make decisions in narrow domains such as medical diagnosis, mineral exploration, and financial analysis. Corporations rushed to build AI departments, and governments launched ambitious national programs. Japan’s Fifth Generation Computer Project, announced in 1982, aimed to create machines capable of reasoning, conversation, and knowledge processing. Investment soared, and for a time it seemed that AI was on the verge of transforming industry. ### The Second Winter: Disillusionment Returns But expert systems had fundamental weaknesses. They were brittle — they broke down when confronted with situations outside their pre-programmed rules. They were expensive to build and even more expensive to maintain. And as desktop personal computers grew more powerful and more affordable throughout the late 1980s, the specialized LISP machines that had underpinned much of the AI industry were rendered obsolete almost overnight. The expert systems market collapsed. Japan quietly wound down its Fifth Generation project in 1992, acknowledging that its most ambitious goals had not been met. The second AI winter set in, and once again the field retreated from public view, sustained only by a handful of determined researchers who continued their work in relative obscurity. ## Looking Back, Looking Forward The cyclical history of artificial intelligence is more than a curiosity. It is a reminder that technological progress rarely follows a straight line. Each spring and summer brought genuine breakthroughs — ideas and systems that advanced our understanding of intelligence and computation. Each winter, though painful, cleared away unrealistic expectations and forced the field to develop more rigorous methods. The researchers who persisted through the winters — refining algorithms, building better datasets, and waiting for hardware to catch up with theory — laid the foundations for the modern AI era that would follow. Understanding these seasons helps us approach today’s AI achievements with both the excitement they deserve and the measured perspective that history demands. *References: Haenlein & Kaplan (2019), “A Brief History of Artificial Intelligence”; Beckman (2002), “A Brief History of AI.”* Share this article Copy link [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Flabelf.ai%2Fblog%2Ffour-seasons-of-ai-the-brief-history-of-artificial-intelligence-i "Share on LinkedIn") [](https://twitter.com/intent/tweet?url=https%3A%2F%2Flabelf.ai%2Fblog%2Ffour-seasons-of-ai-the-brief-history-of-artificial-intelligence-i\&text=Four%20Seasons%20of%20AI%3A%20A%20Brief%20History "Share on X")  Antony Lu Contributor ## More articles [](/blog/treat-people-as-people-at-scale) [AI](/blog/treat-people-as-people-at-scale) ### [We Treat People as People, at Scale](/blog/treat-people-as-people-at-scale) [One-to-one marketing was promised in the 90s and never delivered. Now AI makes it possible — not by automating away the human interaction, but by making every interaction radically smarter.](/blog/treat-people-as-people-at-scale) [T](/blog/treat-people-as-people-at-scale) [The Labelf Team · April 8, 2026](/blog/treat-people-as-people-at-scale) [](/blog/youre-renting-your-customers) [AI](/blog/youre-renting-your-customers) ### [You're Renting Your Customers — And The Price Goes Up Every Year](/blog/youre-renting-your-customers) [While everyone celebrates the shift to self-service and automation, companies are quietly paying more and more to reach the customers they already had. There is another way.](/blog/youre-renting-your-customers) [T](/blog/youre-renting-your-customers) [The Labelf Team · April 2, 2026](/blog/youre-renting-your-customers) [](/blog/nlp-techniques) [AI](/blog/nlp-techniques) ### [NLP Techniques](/blog/nlp-techniques) [An exploration of the most important natural language processing techniques used in modern AI, from tokenization and word embeddings to transformers and large language models.](/blog/nlp-techniques) [F](/blog/nlp-techniques) [Filip Sörlin · November 21, 2022](/blog/nlp-techniques) --- # How Does Deep Learning Work? > A comprehensive overview of deep learning, exploring how neural networks learn from data, the architecture behind modern AI systems, and why deep learning has become the backbone of artificial intelligence. [ Back to blog](/blog)  *** I will try to give you a quite simple explanation of what actually goes on in a deep learning model that could be read by anybody, no matter previous knowledge. Some things are a bit simplified in order to accommodate that. Let’s begin with sorting some terms out. Deep learning is a sub-field within the wider machine learning field. Machine learning is sometimes used when building AI software. AI is however a very broad concept which also involves NPCs in games, such as those little ghosts that try to get you in Pacman. Those ghosts does not have any machine- or deep learning behind their intelligence however.  The goal when doing deep learning is to provide a correct output given a certain input. Such ``-pairs could in the case of self driving cars be: `` or in the case of someone building a stock trading bot be ``.  Let’s do our example on text translation, which is actually done with the help of deep learning in Google Translate. In this case, we want to translate sentences from English to Swedish. The first problem we need to solve is that a deep learning model wants its input converted to a numerical representation. Let’s use this ASCII-table to translate the English letters to numbers before we run it through the deep learning model.  Nice, now let’s send the numbers into what is called a neural network. The neural network consists of something called perceptrons. These perceptrons take many inputs, and each input (seen as x’s in the image below) is multiplied by a weight (seen as w’s in the image). The sum of the results of all these multiplications are run through a quite simple mathematical function called the activation function and a number comes out on the other side.  If you put several of these perceptrons in a net you get the actual artificial neural network. The more layers of perceptrons in a network, the “deeper” it is. And hence the term DEEP learning!  Alright, now it is time to train this model so that it does what we want it to do, translating sentences from English into Swedish. For that we also need a training dataset that holds English sentences and their Swedish translations:  Let’s set those weights (W1-W6 below) to some random numbers and see what happens when we run the sentence “CATS ARE CUTE” through the model:  Oops, that didn’t turn out very well. Let’s try to change the weights to some other random numbers and run it again:  Look! This time i was a bit closer to the answer. Let’s keep changing the numbers in that same “direction”, meaning that if we changed the weight W1 from 0.3 to 0.4, then maybe we should try to set it to 0.5 and see what happens.  IT WORKED! We got the right answer! Now this usually takes thousands of iterations doing this on thousands data points and changing the weights in a network with more layers and perceptrons. But I hope it conveyed the gist of it. The next step is to test our newly trained model on sentence-pairs that it hasn’t seen before (a test set). That’s how you know if it actually works in real life or just on the data you trained on (overfitting). If that all works out, we can deploy it to our translation website! Now I haven’t been completely honest with you. Translation is usually done using two models. But keep reading because this is where it gets really interesting! You normally have one “encoder model” and one “decoder model”. You encode the input sentence into an intermediate representation of the actual meaning of the sentence. You can think of the intermediate representation like one or many dots in one of those Excel dot charts…  …or maybe not exactly. This chart actually has HUNDREDS of dimensions instead of just two like in Excel! That leads to the fact that it is quite hard to portray in an image, but I’ll give it a shot:  Anyway, let’s take this mathematical representation of the sentence’s inner meaning and run it through the decoder model which will then give us the final result:  The really nice thing about this is that we can use the same intermediate representation of the sentence, but put it through another decoder model. Like this one, which will decode it into Spanish instead:  It gets even cooler when we try it with a decoder that is trained to decode into a bunch of pixel values, in other words, a picture of cute cats!  This is (kind of) how Open AI achieved their super impressive results with their DALL-E model that they recently came out with. You can read more about that on [OpenAI’s DALL-E blog post](https://openai.com/blog/dall-e/). DALL-E, GPT-3, Alphafold are all based on a type of Deep Learning model architecture that is called Transformers. Transformer-models were invented in 2017 and have accelerated the frequency of new AI breakthroughs massively since then! Transformers-models is also what we use to power our no code AI tool here at Labelf. Labelf even lets you train a transformer model without knowing anything about code! Or deep learning for that part. But we can’t count you to that crowd now that you’ve made it all the way here! Share this article Copy link [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fhow-does-deep-learning-work "Share on LinkedIn") [](https://twitter.com/intent/tweet?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fhow-does-deep-learning-work\&text=How%20Does%20Deep%20Learning%20Work%3F "Share on X")  Per Näslund CTO & Co-Founder ## More articles [](/blog/treat-people-as-people-at-scale) [AI](/blog/treat-people-as-people-at-scale) ### [We Treat People as People, at Scale](/blog/treat-people-as-people-at-scale) [One-to-one marketing was promised in the 90s and never delivered. Now AI makes it possible — not by automating away the human interaction, but by making every interaction radically smarter.](/blog/treat-people-as-people-at-scale) [T](/blog/treat-people-as-people-at-scale) [The Labelf Team · April 8, 2026](/blog/treat-people-as-people-at-scale) [](/blog/youre-renting-your-customers) [AI](/blog/youre-renting-your-customers) ### [You're Renting Your Customers — And The Price Goes Up Every Year](/blog/youre-renting-your-customers) [While everyone celebrates the shift to self-service and automation, companies are quietly paying more and more to reach the customers they already had. There is another way.](/blog/youre-renting-your-customers) [T](/blog/youre-renting-your-customers) [The Labelf Team · April 2, 2026](/blog/youre-renting-your-customers) [](/blog/nlp-techniques) [AI](/blog/nlp-techniques) ### [NLP Techniques](/blog/nlp-techniques) [An exploration of the most important natural language processing techniques used in modern AI, from tokenization and word embeddings to transformers and large language models.](/blog/nlp-techniques) [F](/blog/nlp-techniques) [Filip Sörlin · November 21, 2022](/blog/nlp-techniques) --- # NLP Techniques > An exploration of the most important natural language processing techniques used in modern AI, from tokenization and word embeddings to transformers and large language models. [ Back to blog](/blog)  *** Natural language processing, commonly known as NLP, is a field of computer science and artificial intelligence concerned with the interactions between computers and human languages. Rather than requiring people to speak the rigid, structured language of machines, NLP flips the equation: it programs computers to understand *our* language — messy grammar, slang, sarcasm, and all. At its core, NLP focuses on teaching machines to process and make sense of large volumes of text and voice data. Every time you ask a virtual assistant a question, run a spell-check, or get a suggested reply in your email, NLP is working behind the scenes. And the technology has come a remarkably long way. Modern NLP models can now perform typical reading comprehension tasks at a level that matches — and sometimes surpasses — human performance. ## Why Should You Pay Attention to NLP? If your business deals with any form of written or spoken communication (and what business doesn’t?), NLP is directly relevant to you. Here are a few reasons it deserves your attention: * **Task automation and cost reduction.** Repetitive language-heavy tasks — reading support tickets, categorizing feedback, extracting data from documents — can be handled by NLP models around the clock without fatigue. * **Enhanced customer service.** Automated responses powered by NLP can resolve common inquiries instantly, freeing up human agents to focus on the issues that truly need a personal touch. * **Better customer understanding.** By analyzing what customers actually write and say in their own words, NLP helps you uncover patterns, pain points, and opportunities that structured surveys often miss. The techniques described below are the building blocks that make all of this possible. Understanding them gives you a practical vocabulary for evaluating which NLP capabilities matter most for your organization. ## Sentiment Analysis Sentiment analysis is the technique of computationally determining whether a piece of text expresses a positive, negative, or neutral opinion. It answers a deceptively simple question: *How does this person feel about what they’re talking about?* At scale, the implications are powerful. Imagine a company receiving thousands of product reviews every week. Manually reading each one is impractical, but sentiment analysis can instantly sort them into categories, surfacing the glowing praise and the harsh criticism alike. **Practical examples:** * **Customer feedback triage.** A support team can automatically flag messages with strongly negative sentiment and route them to senior agents, making sure dissatisfied customers get fast, attentive responses before frustration escalates. * **Brand monitoring.** Marketing teams track sentiment across social media mentions to gauge how a product launch, campaign, or PR event is being received in real time. * **Employee pulse surveys.** HR departments analyze open-ended survey responses to understand workforce morale without requiring someone to read every comment. ## Text Classification Text classification is the process of assigning predefined labels or categories to a piece of text. An algorithm is trained on a set of labeled examples — for instance, emails that have already been sorted into “billing,” “technical support,” and “sales inquiry” — and then learns to apply those same labels to new, unseen text automatically. This is one of the most widely deployed NLP techniques because the use cases are virtually endless. Any time a human is reading text and putting it into a bucket, text classification can likely do the same job faster and more consistently. **Practical examples:** * **Spam filtering.** Your email provider uses text classification to decide whether an incoming message belongs in your inbox or your spam folder. * **Topic categorization.** A news aggregator automatically tags articles as “politics,” “sports,” “technology,” or “business” so readers can find what interests them. * **Support ticket routing.** An incoming customer ticket is classified by topic and urgency, then automatically routed to the right team — no manual sorting required. ## Named Entity Recognition (NER) Named entity recognition, or NER, is the technique of scanning text to locate and classify specific entities into predefined categories. These entities typically include person names, organizations, locations, dates, monetary values, and other structured data points hiding inside unstructured text. Think of NER as a highlighter that reads through a document and marks every name, place, company, and date it finds, then labels each one by type. **Practical examples:** * **GDPR compliance and data masking.** Organizations processing customer communications can use NER to automatically detect and redact personally identifiable information — names, addresses, phone numbers — before the data is stored or shared. * **Contract analysis.** Legal teams use NER to extract party names, effective dates, monetary amounts, and governing jurisdictions from large volumes of contracts, turning hours of manual review into seconds. * **News intelligence.** Media monitoring platforms extract the people, companies, and locations mentioned in news articles to build knowledge graphs and track how entities are connected over time. ## Topic Modeling While text classification assigns labels you define in advance, topic modeling discovers hidden thematic patterns in large collections of text without any predefined categories. It clusters documents into groups based on the words they share, revealing what your data is actually about — even when you don’t know what to look for. This makes topic modeling especially valuable for exploratory analysis, where the goal is to understand the landscape of a dataset rather than to sort it into known categories. **Practical examples:** * **Customer feedback discovery.** A company launches a new product and collects thousands of open-ended responses. Topic modeling reveals the dominant themes — perhaps “battery life,” “ease of setup,” and “missing features” — without anyone having to read every response or guess what topics to look for in advance. * **Research literature review.** Academics use topic modeling to survey large bodies of published papers and identify the main research themes within a field. * **Call center analytics.** By applying topic modeling to transcribed customer calls, operations teams discover recurring conversation themes and can prioritize process improvements where they will have the greatest impact. ## Text Summarization Text summarization is the technique of condensing a document into a shorter version that retains its most essential information and meaning. The goal is to save the reader time without sacrificing the key points. There are two broad approaches: *extractive* summarization, which selects and stitches together the most important sentences from the original text, and *abstractive* summarization, which generates entirely new sentences that paraphrase the source material. Modern NLP models increasingly excel at the abstractive approach, producing summaries that read naturally and capture nuance. **Practical examples:** * **Meeting notes.** After a one-hour meeting, an NLP model generates a concise summary of decisions made, action items assigned, and topics discussed — ready to share with stakeholders who couldn’t attend. * **Legal document review.** Lawyers receive condensed summaries of lengthy contracts, depositions, or regulatory filings, allowing them to quickly assess relevance before diving into the full text. * **News digests.** Media platforms generate brief summaries of long-form articles, giving readers the gist so they can decide which pieces deserve a full read. ## Conclusion Sentiment analysis, text classification, named entity recognition, topic modeling, and text summarization are five foundational NLP techniques that turn unstructured language into structured, actionable insight. Each addresses a different aspect of understanding text, and in practice they are often combined — for example, classifying support tickets by topic while simultaneously scoring them for sentiment, then summarizing the results for a weekly report. The encouraging reality is that you no longer need a team of machine learning engineers to put these techniques to work. Modern platforms have made NLP accessible to business teams who understand their data and their problems, even if they’ve never trained a model. The key is knowing which technique fits which problem — and now you do. Share this article Copy link [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fnlp-techniques "Share on LinkedIn") [](https://twitter.com/intent/tweet?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fnlp-techniques\&text=NLP%20Techniques "Share on X")  Filip Sörlin CAIO & Co-Founder ## More articles [](/blog/treat-people-as-people-at-scale) [AI](/blog/treat-people-as-people-at-scale) ### [We Treat People as People, at Scale](/blog/treat-people-as-people-at-scale) [One-to-one marketing was promised in the 90s and never delivered. Now AI makes it possible — not by automating away the human interaction, but by making every interaction radically smarter.](/blog/treat-people-as-people-at-scale) [T](/blog/treat-people-as-people-at-scale) [The Labelf Team · April 8, 2026](/blog/treat-people-as-people-at-scale) [](/blog/youre-renting-your-customers) [AI](/blog/youre-renting-your-customers) ### [You're Renting Your Customers — And The Price Goes Up Every Year](/blog/youre-renting-your-customers) [While everyone celebrates the shift to self-service and automation, companies are quietly paying more and more to reach the customers they already had. There is another way.](/blog/youre-renting-your-customers) [T](/blog/youre-renting-your-customers) [The Labelf Team · April 2, 2026](/blog/youre-renting-your-customers) [](/blog/what-is-accuracy-precision-recall-and-f1-score) [AI](/blog/what-is-accuracy-precision-recall-and-f1-score) ### [What Is Accuracy, Precision, Recall, and F1 Score?](/blog/what-is-accuracy-precision-recall-and-f1-score) [A clear guide to the most common classification metrics in machine learning: accuracy, precision, recall, and F1 score. Learn when to use each metric and how they relate to real-world model performance.](/blog/what-is-accuracy-precision-recall-and-f1-score) [T](/blog/what-is-accuracy-precision-recall-and-f1-score) [Ted Tigerschiold · November 7, 2022](/blog/what-is-accuracy-precision-recall-and-f1-score) --- # We Treat People as People, at Scale > One-to-one marketing was promised in the 90s and never delivered. Now AI makes it possible — not by automating away the human interaction, but by making every interaction radically smarter. [ Back to blog](/blog)  *** One-to-one marketing has been the holy grail of business for a quarter century. Don Peppers and Martha Rogers wrote the book in 1993. Every CRM vendor since has promised to deliver it. None of them have. What we got instead was segmentation dressed up as personalization. Your customers divided into six personas, each receiving a slightly different email template. “Dear valued customer” with a first name inserted. Recommendation engines that suggest what everyone else bought, not what *this* person actually needs. The technology wasn’t ready. The data wasn’t connected. And the economics didn’t work — you couldn’t afford to treat every customer as an individual when that required a human being with full context for every interaction. All three of those things just changed. ## The promise that was never kept The idea was simple and powerful: treat every customer as a market of one. Understand their individual needs, preferences, and history. Respond accordingly. Build a relationship so strong that switching to a competitor feels like a loss, not a trade. Instead, the industry went the opposite direction. Self-service portals replaced phone calls. Chatbots replaced agents. Every efficiency gain pulled companies further from actually knowing their customers. And with GDPR tightening, cookies dying, and digital channels getting noisier, companies know *less* about their customers today than they did a decade ago. The data that would have powered one-to-one relationships didn’t disappear. It shifted. It’s now embedded in the conversations customers have with your support team, your sales agents, and yes — your chatbots. Every call, chat, and email contains signals about what this specific customer wants, what frustrates them, what they’d buy, and whether they’re about to leave. The problem is that no human can read 500,000 conversations a year and connect the dots. So these signals get lost. And CMOs keep spending on Google Ads to reach the same people who called last Tuesday. ## Why it’s possible now Three things converged to make one-to-one relationships real for the first time. **AI that understands language, not just keywords.** Modern language models don’t search for the word “cancel” — they understand that “I’ve had enough of this” means the same thing. They read a customer saying “we just moved to a bigger house” and connect that to a broadband upgrade opportunity. They detect that an agent’s tone shifted from empathetic to dismissive halfway through a call. This isn’t keyword matching. It’s comprehension. **The economics of scale flipped.** Training a custom model on your conversation data no longer requires a team of data scientists and six months. It requires describing what you want to know. The AI does the rest. When the cost of understanding each customer individually drops to near zero, the only question is whether you’re doing it. **Actions, not reports.** The missing piece was always the last mile. Insight is worthless if it sits in a dashboard nobody checks. What’s changed is the ability to turn understanding into action automatically — a call list, a coaching recommendation, a product alert, a recovery plan — delivered to the person who can act on it, the moment it matters. This is what one-to-one looks like in practice. Not a segment. Not a persona. A specific customer with specific interests, specific frustrations, and a specific opportunity — surfaced automatically from their actual conversations, delivered to the agent who can act on it today. ## What a CMO should care about If you lead marketing, growth, or customer experience at a company with tens or hundreds of thousands of customers, here’s the question: where does your budget go? Most organizations split their spend between acquiring new customers (ads, affiliates, telemarketing) and servicing existing ones (support, retention, loyalty programs). The first bucket gets measured obsessively — CAC, ROAS, conversion rates. The second gets treated as overhead. But the second bucket is where the richest data lives. Your support team talks to more customers in a week than your marketing team reaches in a quarter. And those conversations contain exactly the signals that make one-to-one relationships possible: * **Churn signals** — a customer mentioning a competitor, expressing repeated frustration, downgrading a product. These appear in conversations weeks before they show up in cancellation data. * **Sales signals** — a life change, a product question, an expressed need that maps to something you sell. Your agents hear these every day. Most are never captured. * **Experience signals** — what creates delight (first-call resolution, agents who remember context) and what destroys trust (transfers, repetition, broken promises). When you aggregate these signals per customer — not per segment, per *customer* — you get something that hasn’t existed before: a living, continuously updated understanding of each individual’s relationship with your company. Their satisfaction trajectory. Their risk level. Their revenue potential. And most importantly: what to do about it. ## From cost center to growth engine The hardest shift isn’t technological. It’s organizational. Customer service has been treated as a cost center for so long that most companies can’t imagine it contributing to revenue. But the math is straightforward. A single support conversation where you save a churning customer is worth more than ten Google clicks. A cross-sell that happens naturally during a support call — because the agent knows the customer just moved to a bigger house — converts at five to ten times the rate of cold outreach. A proactive callback to a customer whose issue wasn’t fully resolved doesn’t just prevent churn — it creates the kind of loyalty no campaign can buy. This is the trajectory of a single customer who was frustrated, on the verge of leaving, and recovered through a series of genuine interactions. Not a retention script. Not a generic discount code. A human being who knew the history, understood the problem, and had a plan. The companies that figure this out don’t just retain better — they grow differently. Their customer service function becomes a source of qualified leads, product intelligence, and competitive insight. Their agents become relationship managers, not ticket closers. Their marketing spend shifts from buying attention to amplifying the attention they already have. ## The scale problem, solved differently The objection is always scale. “We can’t treat 400,000 customers individually — we don’t have the staff.” That’s the old frame. The new frame: AI reads every interaction, builds a profile for every customer, and generates the right action for the right person at the right time. The human only shows up where they add the most value — in the conversation itself. This is the difference between using AI to replace human interaction and using AI to *power* human interaction. The agent doesn’t need to read a customer’s full history — the system summarizes it. The agent doesn’t need to guess what to offer — the system recommends it. The agent doesn’t need to decide who to call — the system prioritizes. The human does what humans do best: listen, empathize, adapt, persuade. The AI does what AI does best: read millions of conversations, find patterns, generate plans, track outcomes. Not AI instead of people. AI so that people can be better at being people. ## Industries where this matters most One-to-one at scale isn’t equally valuable everywhere. It matters most in industries where: * **Products are standardized** — when everyone offers more or less the same thing, the relationship is the differentiator. Telecom, utilities, retail banking. * **Customer relationships are long** — subscription businesses where each customer represents years of recurring revenue. Insurance, SaaS, energy. * **Switching costs are low but inertia is high** — customers don’t leave because they found something better. They leave because something went wrong and nobody fixed it. The default is to stay. The risk is in the exceptions. In these industries, the difference between treating customers as segments and treating them as individuals compounds over years. A 2% improvement in retention rate at a large operator translates to millions in preserved revenue. A 5% lift in cross-sell conversion during support interactions creates an entirely new revenue channel from an existing cost center. The companies that figure this out first in each vertical will set a standard their competitors have to match — and matching is always harder than leading. ## What this looks like in practice No theory. Here’s what a company running this model actually sees on a Monday morning: **The churn list.** 47 customers flagged as high risk this week. Each one with a risk score, the specific reasons driving it, recommended actions, and the agent best suited to make the call. Not “Segment B is churning” — “Anna Lindström mentioned switching to a competitor on Thursday. Her contract expires in 12 days. Here’s what to say.” **The opportunity list.** 89 upsell opportunities identified from this week’s conversations. Each with the customer’s profile, the signal that triggered it, and a personalized pitch. Not “customers who bought X also bought Y” — “Erik watches hockey every weekend, his summer house has poor coverage, and he asked about streaming quality. Recommend the sports + mesh WiFi bundle.” **The coaching feed.** Three agents whose quality scores dropped this week. For each: the specific conversations where it happened, what they did differently from top performers, and which playbook to reference in the 1:1. **The process alerts.** A firmware update broke the self-service password reset for a specific device model. 340 calls generated so far this week, trending up. Estimated cost if unresolved for another week: $18,000. The engineering ticket was auto-created on Wednesday. None of this required anyone to build a report, write a query, or log into an analytics tool. It was generated automatically from the conversations that happened this week, compared against everything that came before. ## The question for CMOs The marketing industry has spent two decades optimizing for reach — how many eyeballs, how many clicks, how many impressions. And it worked, until reach became a commodity that gets more expensive every quarter. The next decade belongs to companies that optimize for depth instead. Not how many customers you can reach, but how well you know the ones you already have. Not how many leads you can generate, but how many of the leads your customer service team hands you actually convert. Your customers are already talking to you. Every day. In every call, chat, and email. They’re telling you what they need, what frustrates them, what they’d buy, and whether they plan to stay. The only question is whether you’re listening — and whether you’re treating each of those conversations as what it really is: the most valuable marketing data you have. We treat people as people, at scale. That’s not a tagline. It’s the entire business model. [See the platform →](/platform) Share this article Copy link [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Flabelf.ai%2Fblog%2Ftreat-people-as-people-at-scale "Share on LinkedIn") [](https://twitter.com/intent/tweet?url=https%3A%2F%2Flabelf.ai%2Fblog%2Ftreat-people-as-people-at-scale\&text=We%20Treat%20People%20as%20People%2C%20at%20Scale "Share on X")  The Labelf Team Team ## More articles [](/blog/youre-renting-your-customers) [AI](/blog/youre-renting-your-customers) ### [You're Renting Your Customers — And The Price Goes Up Every Year](/blog/youre-renting-your-customers) [While everyone celebrates the shift to self-service and automation, companies are quietly paying more and more to reach the customers they already had. There is another way.](/blog/youre-renting-your-customers) [T](/blog/youre-renting-your-customers) [The Labelf Team · April 2, 2026](/blog/youre-renting-your-customers) [](/blog/nlp-techniques) [AI](/blog/nlp-techniques) ### [NLP Techniques](/blog/nlp-techniques) [An exploration of the most important natural language processing techniques used in modern AI, from tokenization and word embeddings to transformers and large language models.](/blog/nlp-techniques) [F](/blog/nlp-techniques) [Filip Sörlin · November 21, 2022](/blog/nlp-techniques) [](/blog/what-is-accuracy-precision-recall-and-f1-score) [AI](/blog/what-is-accuracy-precision-recall-and-f1-score) ### [What Is Accuracy, Precision, Recall, and F1 Score?](/blog/what-is-accuracy-precision-recall-and-f1-score) [A clear guide to the most common classification metrics in machine learning: accuracy, precision, recall, and F1 score. Learn when to use each metric and how they relate to real-world model performance.](/blog/what-is-accuracy-precision-recall-and-f1-score) [T](/blog/what-is-accuracy-precision-recall-and-f1-score) [Ted Tigerschiold · November 7, 2022](/blog/what-is-accuracy-precision-recall-and-f1-score) --- # Voice of the Customer: AI-Powered Text Analytics > Discover how Voice of the Customer (VoC) analytics powered by AI can help businesses understand customer sentiment, identify churn drivers, and make data-driven decisions from text feedback. [ Back to blog](/blog)  *** Voice of the Customer, commonly referred to as VoC, represents the full spectrum of what customers express about a brand, product, or service. It encompasses their expectations, their preferences, the feedback they volunteer, and the aversions they develop over time. Every interaction a customer has with your organization — whether it is a support ticket, a product review, a social media comment, or a response to a survey — contains a fragment of this voice. The challenge has never been a shortage of customer expression. It has been an organizational inability to listen at scale, to synthesize what is being said across dozens of channels, and to translate that understanding into action. For companies that get VoC right, the payoff is significant. They can anticipate problems before they escalate, design products that align with actual demand, and build the kind of loyalty that survives competitive pressure. For those that get it wrong — or ignore it entirely — the consequences tend to surface as churn, declining satisfaction scores, and strategic decisions that feel right internally but miss the mark with the people who matter most. ## Difficulties with Interpretability of Numbers Solely Most organizations today collect enormous amounts of data about their customers, but the data arrives in fundamentally different formats. There are behavioral metrics — click rates, session durations, purchase frequencies — and there are qualitative signals — written complaints, chat transcripts, open-ended survey responses. The trouble begins when teams try to draw conclusions from the numerical data alone, without grounding those numbers in the context that only qualitative feedback can provide. Consider a fictional Swedish streaming service we will call Service N. Over the past two quarters, Service N has watched its subscriber base shrink steadily. This decline is puzzling to the leadership team because, by most internal measures, the service has been improving. They have expanded their content library, reduced buffering times, and launched a redesigned mobile app. The behavioral data tells them that average session length is holding steady and that new sign-ups remain healthy. Yet cancellations continue to climb, and the numbers alone offer no satisfying explanation for why. Part of the problem is that human interpretation of numerical data is inherently inconsistent. Two analysts looking at the same churn dashboard may arrive at different conclusions depending on which metrics they prioritize and which assumptions they bring. More fundamentally, behavioral data captures what customers do but not why they do it. Service N can see that a subscriber watched fewer hours in their final month, but they cannot determine from that data point whether the customer left because of pricing, content dissatisfaction, a competitor’s offer, or a technical issue that made the experience frustrating. The connection between observable behavior and underlying motivation remains opaque when numbers are the only lens. ## Why Examine What Customers Already Told You The answer to Service N’s puzzle is almost certainly sitting in data they already possess but have not systematically examined. Customers rarely leave in silence. They post on forums, leave reviews on app stores and aggregator platforms, reach out to support teams, and comment on social media. Each of these touchpoints contains unstructured text — language that is messy, informal, and context-dependent — but language that, when analyzed properly, reveals the precise reasons behind customer behavior. Text mining and natural language processing make it possible to examine these qualitative data sources at scale. Rather than asking a team of analysts to manually read through thousands of reviews and support transcripts, AI can process the entire corpus and extract structured insights. Modern NLP models are capable of interpreting context, detecting sentiment, identifying recurring themes, and distinguishing between a customer who mentions pricing as a passing observation and one who cites it as the reason they are leaving. This is not keyword matching. It is genuine comprehension of what customers are communicating. When Service N turned AI-powered text analytics on their available qualitative data — forum discussions, platform reviews, and inbound support inquiries — a clear picture emerged. The analysis identified five primary drivers of churn: pricing perceptions, content quality and catalog depth, platform navigation and user experience, customer service satisfaction, and technical compatibility across devices. Crucially, the analysis also revealed the relative weight of each driver, something that would have been impossible to determine from behavioral metrics or manual review alone. Service N now had a ranked list of problems to solve, grounded not in internal assumptions but in the actual words of the customers who were leaving. ## Finding Valuable Information From Texts The real power of text analytics becomes apparent when you move beyond static snapshots and begin tracking sentiment and topic frequency across time. By quantifying reviews and feedback across defined timeframes, organizations can detect shifts in customer perception, correlate those shifts with specific events or decisions, and measure the impact of interventions after they are deployed.  When Service N applied this temporal analysis to their data, a striking finding emerged: content quality was a significantly more influential driver of churn than pricing. While pricing complaints were present and persistent, they remained relatively stable over time. Content-related dissatisfaction, on the other hand, showed clear spikes that correlated with periods of high cancellation volume. Customers were not leaving primarily because the service cost too much. They were leaving because they felt the catalog did not justify the cost — a subtle but strategically important distinction. The temporal view also surfaced an anomaly worth investigating. In March, pricing complaints showed a noticeable uptick that did not correspond to any change in Service N’s own pricing structure. Further analysis suggested this increase correlated with competitor activity — specifically, a rival service had launched a promotional campaign offering a lower entry price. This kind of insight is invisible in aggregate satisfaction scores or monthly churn rates, but it becomes actionable when you can trace the shift back to specific language in customer feedback. Service N could now decide whether to respond with a pricing adjustment, a value communication campaign, or a targeted retention offer for the customer segment most influenced by the competitive move. ## Exploiting AI Benefits in Text Analysis The lesson from Service N’s experience — and from the growing body of evidence across industries — is that organizations which rely exclusively on quantitative metrics are working with an incomplete picture. Numbers tell you that something is happening. Text tells you why it is happening. The most effective Voice of the Customer programs are those that incorporate both, using behavioral data to identify where to look and text analytics to understand what they find. AI-powered text analysis does not replace human judgment. It amplifies it. By processing qualitative feedback at a scale and speed that no human team can match, it surfaces the patterns, themes, and sentiment shifts that inform better decisions. It transforms unstructured data — the sprawling, inconsistent, often contradictory mass of things customers say — into structured, actionable intelligence. For organizations willing to invest in this capability, the result is a VoC program that goes beyond measurement and becomes a genuine driver of strategic advantage. The path forward is clear: stop treating text data as a secondary source and start treating it as the primary window into customer intent. The voice of your customers is already there, captured in every review, every support conversation, every comment thread. The question is whether you have the tools and the commitment to listen. Share this article Copy link [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Flabelf.ai%2Fblog%2Funderstand-the-voice-of-the-customers-through-optimizing-text-analytics "Share on LinkedIn") [](https://twitter.com/intent/tweet?url=https%3A%2F%2Flabelf.ai%2Fblog%2Funderstand-the-voice-of-the-customers-through-optimizing-text-analytics\&text=Voice%20of%20the%20Customer%3A%20AI-Powered%20Text%20Analytics "Share on X")  Antony Lu Contributor ## More articles [](/blog/every-team-needs-a-different-dashboard) [Use cases](/blog/every-team-needs-a-different-dashboard) ### [Every Team Needs a Different Dashboard — Here's How to Build Yours](/blog/every-team-needs-a-different-dashboard) [Your Head of Ops needs cost data. Your team leads need agent scores. Your process developers need root cause alerts. One dashboard can't serve them all. Dashboard Studio can.](/blog/every-team-needs-a-different-dashboard) [T](/blog/every-team-needs-a-different-dashboard) [The Labelf Team · June 9, 2023](/blog/every-team-needs-a-different-dashboard) [](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [Use cases](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) ### [AI-Powered Ticket Routing for Customer Support](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [Learn how AI-powered automated ticket routing can improve response times, accuracy, and operational efficiency in customer support, with context-based analysis that understands multiple languages.](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [A](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [Antony Lu · July 6, 2022](/blog/deploy-automated-ticket-routing-for-exceptional-customer-support) [](/blog/treat-people-as-people-at-scale) [AI](/blog/treat-people-as-people-at-scale) ### [We Treat People as People, at Scale](/blog/treat-people-as-people-at-scale) [One-to-one marketing was promised in the 90s and never delivered. Now AI makes it possible — not by automating away the human interaction, but by making every interaction radically smarter.](/blog/treat-people-as-people-at-scale) [T](/blog/treat-people-as-people-at-scale) [The Labelf Team · April 8, 2026](/blog/treat-people-as-people-at-scale) --- # What Is Accuracy, Precision, Recall, and F1 Score? > A clear guide to the most common classification metrics in machine learning: accuracy, precision, recall, and F1 score. Learn when to use each metric and how they relate to real-world model performance. [ Back to blog](/blog)  *** When building a machine learning classification model, choosing the right evaluation metric is just as important as choosing the right algorithm. Accuracy, precision, recall, and F1 score are the four most commonly used metrics, and each tells you something different about how well your model is performing. Understanding the distinction between them is critical for making informed decisions about model quality, especially in domains where errors have real consequences. To make these concepts concrete, let’s imagine we work at “Aarons Animal Classifiers Inc.” Our job is to evaluate four different classification models. Each model is given the same set of six test images and must predict whether each image is an **animal** or **not an animal**. Three of the images are animals (a barn owl, a chihuahua, and a sheepdog) and three are not (a mop, a muffin, and a pineapple). Our goal is to figure out which model performs best — and to do that, we need to understand accuracy, precision, recall, and F1 score.  ## The fundamental concepts: True/False and Positive/Negative Before we can calculate any metric, we need to understand the **confusion matrix** — the foundation that all classification metrics are built on. Every single prediction a model makes falls into one of four categories: * **True Positive (TP):** The model predicted “animal” and it actually was an animal. The model got it right. * **True Negative (TN):** The model predicted “not animal” and it actually was not an animal. The model got it right. * **False Positive (FP):** The model predicted “animal” but it was actually not an animal. The model was wrong — it raised a false alarm. * **False Negative (FN):** The model predicted “not animal” but it was actually an animal. The model was wrong — it missed one. The key to remembering these is simple: **True/False** tells you whether the model was correct or not. **Positive/Negative** tells you what the model predicted. A “False Positive” means the model incorrectly predicted positive.  If a model were perfect — correctly identifying all three animals as animals and all three non-animals as non-animals — the confusion matrix would show 3 True Positives and 3 True Negatives, with zero False Positives and zero False Negatives.  In practice, models make mistakes. A model might correctly identify two animals but miss the third, while also incorrectly labeling the mop as an animal. That would give us 2 True Positives, 2 True Negatives, 1 False Positive, and 1 False Negative.  Now that we understand these four categories, we can define each metric. ## Accuracy **“Out of all predictions made, how many were correct?”** Accuracy is the simplest and most intuitive metric. It counts up all the correct predictions (both True Positives and True Negatives) and divides by the total number of predictions: **Accuracy = (TP + TN) / (TP + TN + FP + FN)** In our animal example, if a model correctly classifies 5 out of 6 images, it has an accuracy of 5/6 = 83.3%. While accuracy is easy to understand, it can be deeply misleading when classes are **imbalanced**. Imagine a dataset with 950 non-animals and 50 animals. A model that blindly predicts “not animal” for every single image would achieve 95% accuracy — but it would be completely useless at the actual task of finding animals. This is why accuracy alone is never enough. ## Precision **“Out of all positive predictions made, how many were actually correct?”** Precision focuses exclusively on the predictions the model labeled as positive. Of all the times the model said “this is an animal,” how often was it right? **Precision = TP / (TP + FP)** High precision means the model rarely cries wolf — when it says something is an animal, you can trust it. Low precision means the model is labeling too many non-animals as animals (lots of false positives). Precision is the metric to prioritize when **false positives are expensive**. For example, a spam filter should have high precision because flagging a legitimate email as spam (a false positive) could mean a user misses something important. ## Recall **“Out of all data points that should be predicted as positive, how many did we correctly predict?”** Recall looks at the problem from the other direction. Of all the images that actually are animals, how many did the model successfully identify? **Recall = TP / (TP + FN)** High recall means the model rarely misses a positive case — it catches almost everything. Low recall means the model is letting too many actual positives slip through (lots of false negatives). Recall is the metric to prioritize when **false negatives are expensive**. For example, a medical screening test for cancer should have high recall because missing an actual case (a false negative) could cost someone their life. Precision and recall are often in tension with each other. A model can trivially achieve 100% recall by predicting everything as positive — but its precision would plummet. Conversely, a model can achieve near-perfect precision by only predicting positive when it is extremely confident — but it will miss many actual positives, tanking recall. ## F1 Score The F1 score resolves the tension between precision and recall by combining them into a single number using the **harmonic mean**: **F1 = 2 × (Precision × Recall) / (Precision + Recall)** Why the harmonic mean instead of a simple average? Because the harmonic mean **penalizes extreme values**. If a model has 100% precision but 10% recall, a simple average would give 55% — which sounds decent. The harmonic mean gives 18.2% — which more accurately reflects how poor the model really is. The F1 score is only high when both precision and recall are reasonably high. The F1 score is especially useful when you do not have a clear reason to favor precision over recall or vice versa, and you want a single balanced metric to compare models. ## Evaluating the four models Now let’s put these metrics to work and evaluate our four animal classification models. ### Model 1: Predict everything as “animal” Model 1 takes the simplest possible approach — it classifies every single image as an animal. The three actual animals are correctly identified (3 True Positives), but the mop, muffin, and pineapple are all incorrectly labeled as animals too (3 False Positives). There are zero True Negatives and zero False Negatives. * **Accuracy:** (3 + 0) / 6 = **50%** * **Precision:** 3 / (3 + 3) = **50%** * **Recall:** 3 / (3 + 0) = **100%** * **F1 Score:** 2 × (0.5 × 1.0) / (0.5 + 1.0) = **66.7%** This model has perfect recall — it never misses an animal — but that is meaningless because it calls everything an animal. It is like a fire alarm that never stops ringing. Sure, it will catch every fire, but it is useless.  ### Model 2: Predict everything as “not animal” Model 2 is the opposite extreme — it classifies every image as not an animal. The three non-animals are correctly classified (3 True Negatives), but all three actual animals are missed (3 False Negatives). There are zero True Positives and zero False Positives. * **Accuracy:** (0 + 3) / 6 = **50%** * **Precision:** 0 / (0 + 0) = **undefined (0%)** * **Recall:** 0 / (0 + 3) = **0%** * **F1 Score:** **0%** Interestingly, this model has the same 50% accuracy as Model 1, even though it behaves in the exact opposite way. This is a perfect example of why accuracy alone can be misleading — two models with identical accuracy can have completely different behaviors. With 0% recall and 0% F1, this model is clearly useless for finding animals.  ### Model 3: Overpredicts non-animals Model 3 is more conservative — it only labels something as an animal when it is very confident. It correctly identifies one animal and correctly classifies all three non-animals, but it misses two of the actual animals. This gives us 1 True Positive, 3 True Negatives, 0 False Positives, and 2 False Negatives. * **Accuracy:** (1 + 3) / 6 = **66.7%** * **Precision:** 1 / (1 + 0) = **100%** * **Recall:** 1 / (1 + 2) = **33.3%** * **F1 Score:** 2 × (1.0 × 0.333) / (1.0 + 0.333) = **50%** This model has perfect precision — every time it says “animal,” it is correct. But it achieves this by being overly cautious, missing two out of three animals. If you absolutely cannot tolerate false positives, this model might work, but it misses most of the actual animals.  ### Model 4: Overpredicts animals Model 4 leans the other way — it is more aggressive about predicting animals. It correctly identifies all three animals and two of the non-animals, but incorrectly classifies one non-animal as an animal. This gives us 3 True Positives, 2 True Negatives, 1 False Positive, and 0 False Negatives. * **Accuracy:** (3 + 2) / 6 = **83.3%** * **Precision:** 3 / (3 + 1) = **75%** * **Recall:** 3 / (3 + 0) = **100%** * **F1 Score:** 2 × (0.75 × 1.0) / (0.75 + 1.0) = **85.7%** Model 4 achieves the highest accuracy (83.3%), the highest F1 score (85.7%), and perfect recall (100%). It catches every single animal, with the trade-off of one false positive. For most use cases, this is the best model of the four.  ## Conclusion So which model is the “best”? It depends entirely on the use case. * If missing an animal is unacceptable (e.g., detecting dangerous wildlife on a hiking trail), you want **high recall** — Model 4 is your best choice. * If false alarms are unacceptable (e.g., flagging content for manual review where reviewer time is expensive), you want **high precision** — Model 3 would be preferred. * If you want a single balanced metric, the **F1 score** gives you the best of both worlds — and Model 4 wins with 85.7%. * **Accuracy** is a useful starting point, but as we saw with Models 1 and 2, two models can have identical accuracy while being wildly different in actual usefulness. The key takeaway: never rely on a single metric. Understand what each metric measures, consider what kind of errors matter most in your specific context, and choose accordingly. Share this article Copy link [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fwhat-is-accuracy-precision-recall-and-f1-score "Share on LinkedIn") [](https://twitter.com/intent/tweet?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fwhat-is-accuracy-precision-recall-and-f1-score\&text=What%20Is%20Accuracy%2C%20Precision%2C%20Recall%2C%20and%20F1%20Score%3F "Share on X")  Ted Tigerschiold COO & Co-Founder ## More articles [](/blog/treat-people-as-people-at-scale) [AI](/blog/treat-people-as-people-at-scale) ### [We Treat People as People, at Scale](/blog/treat-people-as-people-at-scale) [One-to-one marketing was promised in the 90s and never delivered. Now AI makes it possible — not by automating away the human interaction, but by making every interaction radically smarter.](/blog/treat-people-as-people-at-scale) [T](/blog/treat-people-as-people-at-scale) [The Labelf Team · April 8, 2026](/blog/treat-people-as-people-at-scale) [](/blog/youre-renting-your-customers) [AI](/blog/youre-renting-your-customers) ### [You're Renting Your Customers — And The Price Goes Up Every Year](/blog/youre-renting-your-customers) [While everyone celebrates the shift to self-service and automation, companies are quietly paying more and more to reach the customers they already had. There is another way.](/blog/youre-renting-your-customers) [T](/blog/youre-renting-your-customers) [The Labelf Team · April 2, 2026](/blog/youre-renting-your-customers) [](/blog/nlp-techniques) [AI](/blog/nlp-techniques) ### [NLP Techniques](/blog/nlp-techniques) [An exploration of the most important natural language processing techniques used in modern AI, from tokenization and word embeddings to transformers and large language models.](/blog/nlp-techniques) [F](/blog/nlp-techniques) [Filip Sörlin · November 21, 2022](/blog/nlp-techniques) --- # You're Renting Your Customers — And The Price Goes Up Every Year > While everyone celebrates the shift to self-service and automation, companies are quietly paying more and more to reach the customers they already had. There is another way. [ Back to blog](/blog)  *** Here’s a number most CMOs don’t want to look at too closely: customer acquisition cost has roughly doubled in the past five years across telecom, banking, insurance, and retail. Not because markets got smaller. Because you’re paying to reach people who were already your customers. Think about that for a moment. You had a relationship with these people. They called you, they walked into your stores, they emailed your support team. You knew their names, their problems, their preferences. And then you optimized it all away. ## How we got here The shift to self-service felt like the right call at the time. Every contact deflected was money saved. Fewer agents meant lower overhead. The digital transformation playbooks all pointed the same direction: reduce human touchpoints, increase automation, drive customers online. It worked — for the cost line. But nobody ran the full equation. What you saved on customer service, you now spend on Google. What you gained in operational efficiency, you lost in customer intelligence. Every interaction you deflected was also a data point you surrendered, a relationship signal you’ll never see again. And with GDPR tightening, cookies dying, and digital channels getting noisier by the quarter, you know less about your customers today than you did five years ago. The irony is almost too clean: companies spent a decade minimizing customer contact, and now they spend fortunes trying to buy it back. Retargeting ads, affiliate programs, telemarketing campaigns — all of it is just renting access to people who once talked to you for free. The numbers speak for themselves. For a typical mid-size operator, the monthly revenue at risk from churn alone dwarfs what most companies spend on customer acquisition. The customers you’re losing were already talking to you — and telling you exactly why they were about to leave. ## The false choice The industry’s current answer makes this worse, not better. The pitch from every AI vendor at every conference goes something like this: “Replace your remaining agents with bots. Full automation. Zero cost per interaction.” And sure, there are interactions that should be automated. Password resets. Delivery tracking. Simple FAQ lookups. Nobody needs a human for those. But something strange happens when you automate the easy stuff away. The remaining human interactions don’t become less important — they become more important. They’re the ones where a customer is frustrated enough to demand a real person. Where someone is considering leaving. Where there’s an upsell opportunity buried in a complaint. Where the relationship is actually at stake. These interactions are now scarce. And like anything scarce, their value has gone up. Yet the industry treats them as a cost to be eliminated rather than an asset to be maximized. CMOs are forced into what feels like a binary choice: slash customer service for cost advantage, or keep human agents and accept the overhead. Automate everything, or fall behind. That’s a false choice. ## The contrarian bet We believe the opposite. We think the companies that will dominate the next decade in telecom, banking, insurance, and utilities are not the ones that eliminated customer interaction — they’re the ones that figured out how to make every interaction count. Consider the math. A single customer service conversation where you save a churning customer is worth what — ten Google clicks? Twenty? A cross-sell that happens naturally during a support call converts at five to ten times the rate of a cold outreach. An agent who knows a customer’s history, frustration, and product usage before the call even starts doesn’t just resolve faster — they build the kind of loyalty no ad campaign can buy. This is what it looks like when a company treats every interaction as an investment in the relationship rather than a cost to minimize. A frustrated customer becomes a promoter — not through a marketing campaign, but through a series of genuine human interactions where someone actually cared. The data backs this up. In industries with standardized products — where everyone offers more or less the same thing — the customer relationship is the last real differentiator. Not the product, not the price, not the brand. The experience of being treated like a person who matters. And here’s what’s changed: AI now makes it possible to do this at scale. Not by replacing the human interaction, but by making every human interaction radically better. Knowing who’s at risk before they call. Having the full picture of a customer’s journey before the conversation starts. Coaching agents in real time on what works. Generating proactive outreach — “call this customer about this, right now” — based on signals nobody could spot manually across millions of interactions. The patterns are clear. The things that create lasting customer value — first-call resolution, agents who remember context, proactive follow-ups — are all fundamentally human. The things that destroy it — transfers, repetition, dead ends — are all symptoms of systems that treat interactions as tickets to be closed rather than relationships to be built. This isn’t about spending more on customer service. It’s about recognizing that the conversations you’re already having are the most valuable asset sitting on your books — and treating them accordingly. ## The question If you’re a CMO, here’s what I’d want to know: how much of your budget goes toward buying back access to customers you already had? And how much goes toward maximizing the value of the customers who are already talking to you? Most organizations spend aggressively on acquisition and almost nothing on making existing interactions count. They rent attention from Google and Meta while ignoring the organic attention they get for free every time a customer picks up the phone. The rent keeps going up. The attention you already have is free. The gap between those two numbers is either your biggest liability or your biggest opportunity. You get to choose which one. Share this article Copy link [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fyoure-renting-your-customers "Share on LinkedIn") [](https://twitter.com/intent/tweet?url=https%3A%2F%2Flabelf.ai%2Fblog%2Fyoure-renting-your-customers\&text=You're%20Renting%20Your%20Customers%20%E2%80%94%20And%20The%20Price%20Goes%20Up%20Every%20Year "Share on X")  The Labelf Team Team ## More articles [](/blog/treat-people-as-people-at-scale) [AI](/blog/treat-people-as-people-at-scale) ### [We Treat People as People, at Scale](/blog/treat-people-as-people-at-scale) [One-to-one marketing was promised in the 90s and never delivered. Now AI makes it possible — not by automating away the human interaction, but by making every interaction radically smarter.](/blog/treat-people-as-people-at-scale) [T](/blog/treat-people-as-people-at-scale) [The Labelf Team · April 8, 2026](/blog/treat-people-as-people-at-scale) [](/blog/nlp-techniques) [AI](/blog/nlp-techniques) ### [NLP Techniques](/blog/nlp-techniques) [An exploration of the most important natural language processing techniques used in modern AI, from tokenization and word embeddings to transformers and large language models.](/blog/nlp-techniques) [F](/blog/nlp-techniques) [Filip Sörlin · November 21, 2022](/blog/nlp-techniques) [](/blog/what-is-accuracy-precision-recall-and-f1-score) [AI](/blog/what-is-accuracy-precision-recall-and-f1-score) ### [What Is Accuracy, Precision, Recall, and F1 Score?](/blog/what-is-accuracy-precision-recall-and-f1-score) [A clear guide to the most common classification metrics in machine learning: accuracy, precision, recall, and F1 score. Learn when to use each metric and how they relate to real-world model performance.](/blog/what-is-accuracy-precision-recall-and-f1-score) [T](/blog/what-is-accuracy-precision-recall-and-f1-score) [Ted Tigerschiold · November 7, 2022](/blog/what-is-accuracy-precision-recall-and-f1-score) --- # Book a demo > See Labelf in action. Book a personalized demo to learn how AI interaction analytics can reduce churn and grow revenue. See how Labelf can transform your customer interactions into actionable insights. --- # Let's talk. > Get in touch with the Labelf team. Questions about the platform, need a demo, or want to discuss a partnership — we're here. Contact  Viktor Alm CEO & Co-Founder [ ](mailto:viktor@labelf.ai?subject=Interest%20in%20Labelf%20\(Website\))[](https://www.linkedin.com/in/viktoralm/)  Niklas Cumzelius Sales [ ](tel:+46709218154)[ ](mailto:niklas@labelf.ai?subject=Interest%20in%20Labelf%20\(Website\))[](https://www.linkedin.com/in/niklas-cumzelius-098542/)  Ted Tigerschiold Operations [ ](mailto:ted@labelf.ai?subject=Interest%20in%20Labelf%20\(Website\))[](https://www.linkedin.com/in/ted-tigerschiold/) --- # From raw conversations to business results. > From raw conversations to business results. Explore Labelf's AI platform for interaction analytics, custom models, and dashboards. The Platform Labelf reads every customer interaction, trains custom AI models to understand them, and turns that understanding into actions — churn saves, sales leads, coaching plans, and process fixes. All measured in dollars. [Book a Demo ](/book-a-demo)  ## From description to production in minutes. Describe what you want to classify. The model starts working immediately. No data science required. [See model training →](/platform/model-training) ## Label smarter, not harder. The system finds the examples that matter most — edge cases, rare patterns, your mistakes. 200 smart labels beat 2,000 random ones. [See active learning →](/platform/model-training) ## Search by meaning, not keywords. Type a question. Find matching conversations across millions of interactions — even when the words are completely different. [See AI search →](/platform/ai-search) ## Ask anything. Get answers in seconds. The AI Agent queries your data, reads transcripts, builds charts, and suggests actions — all from a single question. [See the AI agent →](/platform/ai-agent) ## From insight to action. Automatically. When the AI finds a pattern — it writes the playbook, cites real conversations, and tracks whether agents follow it. [See playbooks and alerts →](/platform/playbooks) ## See everything. Build anything. 25+ chart types, custom metrics, real-time filters. Every model you train becomes a dashboard dimension. [See Dashboard Studio →](/platform/dashboards) ## Every customer. Full picture. Churn risk, upsell score, interaction history, AI-generated actions — all in one view per customer. [See customer profiles →](/platform/customer-profiles) ## Deploy anywhere. Trust everything. Cloud, private cloud, or on-premise. 90+ integrations. GDPR compliant. EU-based. Your data never leaves your control. [Integrations](/platform/integrations) [90+ tools — CRM, ticketing, call center, BI, data lakes. API-first.](/platform/integrations) [Explore integrations →](/platform/integrations) [Security & Privacy](/platform/security) [Cloud, private cloud, or on-premise. GDPR. EU AI Act. No US jurisdiction.](/platform/security) [Explore security & privacy →](/platform/security) [Transcription](/platform/transcription) [100+ languages. Speaker separation. Custom vocabulary. Fine-tunable.](/platform/transcription) [Explore transcription →](/platform/transcription) [Data & Export](/platform/data-export) [CSV, API, webhooks. Your data, always. Zero vendor lock-in.](/platform/data-export) [Explore data & export →](/platform/data-export) ## Deep dives into every capability. [AI Search → ](/platform/ai-search)[Model Training → ](/platform/model-training)[Model Evaluation → ](/platform/model-evaluation)[Auto-categorization → ](/platform/auto-categorization)[AI Agent → ](/platform/ai-agent)[Playbooks & Alerts → ](/platform/playbooks)[Dashboards & Studio → ](/platform/dashboards)[Customer Profiles →](/platform/customer-profiles) ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Ask anything. Get answers. > Ask anything about your customers and get instant answers. Labelf's AI agent reads every conversation so your team doesn't have to. AI Agent You have a question about your customers. Ask in plain language and get an answer in seconds — with charts, citations, and recommended actions. [Book a Demo ](/book-a-demo)  See it in action ## All of Labelf's power in one conversation The agent has access to every model, every metric, every conversation. It writes SQL, reads transcripts, builds charts, and suggests actions — all from a single question. Superpowers ## A research agent that can read every conversation. It has read every call, every chat, every ticket. Ask why customers leave, what your best agents do differently, or where your process breaks — and get answers backed by real conversations. Queries your data Writes and executes SQL against your analytics database. Self-corrects if a query fails. Reads conversations Pulls actual transcripts and quotes specific customer statements with dates and context. Builds charts Generates bar charts, line charts, tables — whatever best tells the story of the data. Cross-references models Uses all your trained models as dimensions. "Show frustrated billing customers on mobile." Suggests actions Recommends playbooks, process alerts, and next steps based on what it finds. Multi-step reasoning Breaks complex questions into steps. Plans, executes, and assembles a complete answer. What you can ask ## Any question about your customer interactions. "Why is churn up this month?" Investigation "Which agents handle billing disputes best?" Benchmarking "Show me the top 10 cost drivers this quarter" Reporting "What do customers say about our new pricing?" Conversation analysis "Create a coaching plan for Johan based on his calls" Coaching "How does team Alpha compare to team Beta on FCR?" Comparison "What happened with TV app calls last Tuesday?" Spike analysis "Show me customers who mentioned a competitor this week" Search "What's the ROI of our retention playbook?" Impact measurement ## Connected to everything. Grounded in your data. The agent doesn't make things up. Every number comes from a SQL query you can inspect. Every quote comes from an actual conversation you can read. Every chart is built from your real data. Data Layer SQL queries against your analytics database with self-correction on errors Conversation Layer Reads full transcripts from search index with anti-hallucination safeguards Model Layer All your trained models become queryable dimensions and filter criteria Action Layer Generates charts, playbooks, process alerts, and dashboard filters as output ## The agent is only as smart as the intelligence behind it. Every model you [train](/platform/model-training), every category the system [discovers](/platform/auto-categorization), every pattern in the [dashboards](/platform/dashboards) — the agent uses them all. The more you build, the smarter it gets. ## Days of analysis. Seconds to answer. What used to require an analyst, a SQL expert, and a week of work — the agent does in one conversation. 5 Steps per investigation <30 s Average answer time 0 Answers without source data ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Find what you mean. > Search conversations by meaning, not keywords. Labelf's semantic search finds interactions that matter, even when the words differ. AI Search [Book a Demo ](/book-a-demo) The problem Keyword search finds what you type. But customers don't use your terminology. They say **"I've had enough"** — not **"service cancellation"**. Labelf finds what you mean, across millions of conversations, in seconds.  See it in action ## Search by meaning, not keywords Type a question, a description, or even a feeling. Labelf understands the intent and finds matching conversations — even when the words are completely different. Three search modes ## Keywords when you need precision. Meaning when you don't. Labelf automatically switches between search modes based on your query. Short and specific? Exact match. Longer and descriptive? Semantic understanding. You don't have to think about it. Keyword 1-3 words. Exact match with wildcard support. \*stream\* finds streaming, livestream, mainstream. Semantic 4+ words. Meaning-based. "unhappy with delivery" finds "package never arrived" and "where is my order?". Hybrid Combine both. "\*competitor\* unhappy switching" — exact brand match with semantic intent. Why it matters ## Not just search. Discovery. When you have 5 million conversations, you can't read them. You need a way to ask questions and get answers. AI Search is your entry point into the data. Needle in the haystack Find 200 fraud cases in 5 million conversations. Search by behavior pattern, not just keywords. Cross-language Search in English, find results in Swedish, Norwegian, German — any of 100+ languages. The meaning transfers. Context-aware Understands full conversations, not isolated messages. The meaning of "I'm done" depends on what came before it. Feed into models Search results become training data. Find 200 examples of a pattern, label them, train a custom model. Search is step one. ## Search is how you explore. Models are how you scale. Every search is a hypothesis. When you find a pattern, Labelf helps you turn it into a custom model that runs automatically on every new interaction — no manual work, no data science team required. * Search → Find → Label → Train Use search results as training examples. The model learns from what you find. * No data science required Your domain experts search, label, and train. The platform handles the rest — zero-shot, active learning, fine-tuning. * From ad-hoc to automated What starts as a search query becomes a deployed model classifying every interaction in real-time. ## Search is step one. Training is step two. AI Search helps you explore and discover. When you find a pattern worth tracking, [Custom Model Training](/platform/model-training) turns it into an automated classifier. Together, they form the intelligence engine behind every Labelf solution. ## Stop reading. Start finding. Your conversations contain every answer your business needs. You just couldn't search them properly — until now. 100 + Languages supported 5 M+ Conversations searchable <3 s Average search time ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Find what you didn't know to look for. > Automatically categorize every customer interaction with AI. Discover contact reasons and patterns you didn't know to look for. Auto-categorization Auto-categorization reads all your interactions and discovers categories for you — no training, no labels, no instructions. It finds the patterns you didn't know existed. [Book a Demo ](/book-a-demo)  See it in action ## Point it at your data. Get categories back. The AI reads every conversation, finds recurring patterns, names them, ranks them by volume and cost, and flags what's new. No setup required. Two approaches, one platform ## Auto-discover and custom-train. Together. Auto-categorization finds what's happening. Custom models classify it your way. The two work side by side — the AI discovers patterns you didn't expect, and your trained models enforce the structure you need. Auto-categorization AI reads everything and groups it. No instructions, no labels. Finds what you didn't know to look for. Best for exploration, discovery, and finding blind spots. Custom models You define the categories. Train with examples. Get production-grade accuracy. Best for KPIs, dashboards, playbooks, and anything that needs to be precise. What it discovers ## The things you didn't know were costing you money. Auto-categorization doesn't confirm what you already know. It finds what nobody noticed — because no one was looking for it. Recurring micro-issues "Router won't connect after restart" is 12% of tech support — but it was buried inside "broadband issues" in your old categories. Seasonal spikes Summer house internet setup calls spike 340% in May. Visible only when the AI separates it from general "new service" requests. Process breakdowns "App crashes on firmware v3.2" — the AI found it in 1,200 calls. Your old keyword search missed it because customers said "freezes", "stops working", "black screen". Competitor mentions The AI discovered a cluster of 1,400 calls mentioning a competitor's new pricing — before marketing knew about the campaign. Billing confusion patterns 2,100 calls about "wrong amount on invoice" — but 60% are actually about VAT changes, not billing errors. Completely different fix needed. Hidden churn signals "Just checking my contract end date" — seems innocent, but the AI found these customers churn at 4x the normal rate within 30 days. From discovery to action ## Found something interesting? Make it permanent. When auto-categorization discovers a pattern worth tracking, you can turn it into a custom model with one click. The auto-discovered examples become your initial training data. 1 Auto-categorization discovers a pattern "Router won't connect after restart" — 2,847 occurrences found 2 You promote it to a custom model Auto-discovered examples become training data. Refine with Active Learning. 3 It runs in production, feeds dashboards and alerts From "we didn't know this existed" to "we track it in real-time" — in hours. ## Discovery feeds everything. Auto-categorization is the first step. What it finds flows into [Custom Model Training](/platform/model-training) for precision, [Dashboards](/platform/dashboards) for monitoring, and [Contact Reasons](/solutions/contact-reasons) for the full breakdown. ## You can't fix what you don't know exists. Auto-categorization eliminates blind spots. It reads everything and tells you what's really happening — without assumptions, without bias, without missing anything. 247 Categories auto-discovered 0 Labels required 30 d Average integration window ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Every customer. Full picture. > See every customer's full history — sentiment trends, risk signals, and opportunities in one actionable view. Built automatically. Customer Profiles Your CRM shows what they bought. Your ticketing system shows what they asked. But no one shows the full relationship — every conversation, every frustration, every opportunity, every risk signal — aggregated into one actionable view. [Book a Demo ](/book-a-demo)  See it in action ## Click any customer. Know everything. Churn risk, upsell score, CSAT trend, product portfolio, interaction history, AI-generated actions — all in one view, updated with every new conversation. 360-degree view ## Built from conversations, not forms. Most customer profiles are what you manually entered. Labelf profiles are built from what actually happened — every call, every chat, every ticket, analyzed and aggregated automatically. Churn risk score AI-scored from conversation signals, not just tenure Upsell opportunity Product gaps, expressed interests, timing signals Satisfaction trajectory CSAT over time — trending up, down, or volatile? Full interaction history Every call, chat, ticket — with agent, outcome, and conversation link Product portfolio What they have, what they don't, and what they've asked about AI-generated actions Specific next steps — retention, upsell, or resolution Everywhere you need them ## Profiles power every solution. [Churn risk list](/solutions/churn-reduction) [Click any at-risk customer to see their full profile — why they're at risk and what to do about it.](/solutions/churn-reduction) [Sales opportunity list](/solutions/sales-opportunities) [Click any lead to see their interests, product gaps, and a personalized pitch recommendation.](/solutions/sales-opportunities) [Agent coaching](/solutions/agent-coaching) [See which customers a specific agent has handled — their outcomes, CSAT, and what went well or badly.](/solutions/agent-coaching) [AI Agent answers](/platform/ai-agent) ["Tell me about customer Erik Lindqvist" — the agent pulls the full profile and answers in context.](/platform/ai-agent) [Dashboard drill-down](/platform/dashboards) [Click any data point in a dashboard to see the customers behind it — not just aggregated numbers.](/platform/dashboards) [Playbook tracking](/platform/playbooks) [See which customers were handled with which playbook — and whether it worked.](/platform/playbooks) ## The customer is the center of everything. Every [model](/platform/model-training) prediction, every [dashboard](/platform/dashboards) metric, every [playbook](/platform/playbooks) action connects back to a customer. Profiles are where all the intelligence converges into one actionable view. ## Treat every customer as someone you know. At scale. Automatically. Updated with every new interaction. 360 ° Customer view 0 Manual data entry 30 d Average integration window ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # See everything. Build anything. > Build custom dashboards from any data point in your conversations. Drag, drop, and share real-time insights across your organization. Dashboards & Studio Every team needs different views. Labelf's Dashboard Studio lets you build exactly what each stakeholder needs — from templates or from scratch. [Book a Demo ](/book-a-demo)  See it in action ## KPIs, charts, tables — all live Real-time dashboards powered by your models. Every dimension is a filter. Every metric has a target. Every alert has a cost. Build anything ## Templates to start. Studio to customize. Start with a pre-built template — Churn, Sales, Contact Reasons, Agent Performance. Then customize: add charts, change metrics, adjust filters. Or build from scratch with the full Studio editor. 25+ chart types Line, bar, pie, gauge, treemap, radar, funnel, tables, KPI cards Custom metrics Sum, count, average, ratio, percentile — with targets and alerts Metric alerts Set targets, get alerted when metrics go off track Every dimension is a filter Product, team, agent, category, time — filter and slice instantly Drill down to conversations Click any data point to see the actual interactions behind it Templates for fast start Pre-built dashboards for churn, sales, contact reasons, agents Every role, every view ## Different people need different dashboards. Head of Operations Cost trends, AHT, volume spikes, top issues, waste anatomy. Ranked by business impact. Team Leader Team vs org comparison, per-agent performance, coaching priorities, playbook adherence. Head of Sales Conversion rates, revenue per agent, campaign performance, upsell pipeline. Process Developer Root cause deep dives, process alerts, deflection opportunities, trend monitoring. ## Two data layers. Infinite views. Every dataset generates two analytics tables: conversation-level and utterance-level. Dashboards can query either — aggregated KPIs from conversations, or detailed analysis from individual speaker turns. Conversation level One row per interaction. All model predictions, metadata, KPIs, and outcomes aggregated. Perfect for dashboards and trends. Utterance level One row per speaker turn. What did the agent say? What did the customer say? Per-utterance sentiment and classification. Model columns Every model you train becomes a queryable column. Clusters, tags, scores — all available as chart dimensions and filters. ## Dashboards show what the models find. Every [custom model](/platform/model-training) you train becomes a dashboard dimension. The [AI Agent](/platform/ai-agent) can set dashboard filters from chat. And [Playbooks](/platform/playbooks) track their impact right here. ## Your data. Your dashboards. Your way. No more waiting for the BI team. Build the view you need, with the metrics that matter, filtered to the slice you care about. 25 + Chart types available ∞ Filter combinations 30 d Average integration window ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Your data. Always. > Export enriched interaction data to any BI tool or data warehouse. Your data stays yours — always accessible, fully portable.  Data & Export Every classification, every score, every transcript — it's **your data**. Export it anytime, in any format. Push it to your data lake. Pull it via API. We enrich your data, we don't hold it hostage. [Book a Demo ](/book-a-demo) Get your data out ## Every way you could want it. CSV / Excel Export Download any dataset, any dashboard, any model output as CSV or Excel. Filter first, export the slice you need. REST API Full programmatic access. Push data in, pull classifications out, trigger training, query dashboards. Everything the UI does, the API does. Webhooks Get notified when classifications complete, models deploy, or alerts trigger. Push results to Slack, Teams, or your own systems in real-time. Data Lake Integration Push enriched data directly to your Snowflake, BigQuery, Redshift, or S3 bucket. Labelf becomes a data enrichment layer in your pipeline. Scheduled Reports Set up automated exports on a schedule — daily, weekly, monthly. Delivered to email, SFTP, or cloud storage. Batch Processing Upload historical data in bulk. Process millions of records. Get classifications back as enriched datasets ready for your BI tools. What you get ## Not just raw data. Enriched data. When you export from Labelf, you don't get what you put in — you get what you put in plus everything Labelf added. Every model prediction, every score, every cluster, every tag. Enriched export example | Original | + Labelf adds | | --------------- | ----------------------------- | | Call transcript | Speaker-separated, anonymized | | Customer ID | Churn risk score (0-100) | | Agent name | Performance metrics per model | | Duration | Category (L1/L2/L3) | | Date | Root cause classification | | CSAT score | Sentiment analysis | | | Sales opportunity score | | | Playbook adherence | | | Cluster assignments | Data retention ## You decide how long we keep it. Set retention policies per data type. Keep transcripts forever, delete audio after 30 days, purge everything after a year. Your compliance team sets the rules, Labelf enforces them automatically. Audio files Delete after transcription, keep 30/90/365 days, or retain indefinitely Transcripts Anonymized or raw. Keep as long as you need them for model training and analysis Classifications Model predictions and scores. Typically retained for dashboard and trend analysis Training data Labels and examples. Retained as long as models are active. Exportable anytime ## Zero lock-in. Full portability. If you ever want to leave — take everything with you. Every model, every classification, every transcript, every label. We believe you stay because the product is good, not because your data is trapped. Export all data One click. Everything. CSV, JSON, or via API. Export models Your training data and configurations. Fully portable. Export labels Every label your team created. Your work, your intellectual property. Delete on request Full GDPR-compliant data deletion. Everything wiped, confirmed in writing. ## We enrich your data. We don't own it. Full data sovereignty. Export everything, delete everything, integrate with everything. Your data, your rules, always. 6 Export formats 0 Vendor lock-in 100 % Data portability ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Connects to everything you already use. > Connect Labelf to your existing stack in minutes. Native integrations with Zendesk, Salesforce, Genesys, and 50+ platforms. Integrations Labelf sits on top of your existing systems. We connect to them, read the data, add intelligence, and push actions back. API-first, always. [Book a Demo ](/book-a-demo)  Contact Center 15  Genesys  NICE CXone  Five9  Talkdesk  Avaya  Cisco Webex CC  Amazon Connect  Vonage  RingCentral  8x8  Dialpad  Aircall  CloudTalk  Puzzel  Mitel CRM 7  Salesforce  HubSpot  Microsoft Dynamics 365  Zoho CRM  Pipedrive  Freshsales  SugarCRM Ticketing & Helpdesk 10  Zendesk  Freshdesk  ServiceNow  Jira Service Management  Intercom  Front  Help Scout  Kayako  LiveAgent  Dixa Survey / NPS / CSAT / VoC 11  Medallia  Qualtrics  SurveyMonkey  Typeform  Nicereply  AskNicely  Delighted  Wootric  CustomerGauge  GetFeedback  InMoment Workforce Management 6  Calabrio  Verint  NICE WFM  Assembled  Playvox  Teleopti Cloud & Data Lake 11  AWS S3  AWS Redshift  Google BigQuery  Google Cloud Storage  Azure Blob  Azure Synapse  Snowflake  Databricks  Apache Kafka  PostgreSQL  OpenSearch Communication 4  Microsoft Teams  Slack  Zapier  Make (Integromat) BI & Analytics 7  Tableau  Power BI  Looker  Apache Superset  Grafana  Qlik  Metabase Data Formats 7 CSV JSON Excel (XLSX) XML REST API Parquet Avro 90+ tools across 9 categories And anything else via API API-first ## Don't see your tool? We have an API. Every feature in Labelf is accessible via REST API. Push data in, pull insights out, trigger actions, export results. Build any integration your setup requires. Push data in Upload conversations, tickets, CSAT data via API. Real-time or batch. Pull insights out Get classifications, scores, and predictions programmatically. Trigger actions Webhooks, Salesforce tags, Teams messages — push results back to your systems. ## Data in. Intelligence out. Labelf sits between your data sources and your action systems. We read from everything on the left, add intelligence in the middle, and push results to everything on the right. Data Sources Call recordings Chat transcripts Email tickets CSAT surveys CRM data IVR logs Labelf Intelligence Transcription Classification Scoring Analysis Recommendations Playbooks Action Outputs CRM tags & scores Dashboard KPIs Agent coaching Process alerts Teams/Slack Data lake export ## Keep what works. Add what's missing. No migration. No disruption. Labelf adds intelligence to your existing stack — you keep everything you already have. 90 + Tools supported 0 Systems to replace 30 d Average integration window ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Trust what you deploy. > Measure AI accuracy before you deploy. Validate, compare, and trust your models with full transparency — per class, per example.  Model Evaluation & Trust AI that you can't explain is AI you can't trust. Labelf shows you **exactly** where your models are right, where they're wrong, and where they're uncertain — per class, per confidence level, per example. [Book a Demo ](/book-a-demo) See it in action ## Every class. Every metric. Not just an overall accuracy number. See which categories the model nails, which ones need more examples, and which ones it confuses with each other. Full transparency ## No black boxes. No blind trust. Every model in Labelf comes with full evaluation tools. You decide when a model is good enough. You see where it struggles. And the system helps you fix it. Confusion matrix See which classes get mixed up, click into examples Confidence threshold Slide to trade precision for recall — find the right balance Check labeling The system flags when it disagrees with your labels Per-class metrics F1, precision, recall for every category individually Click into errors See the actual conversations the model got wrong Weak class alerts Flags classes that need more examples or clearer boundaries Drill down ## Click any error. See why. The confusion matrix shows where the model mixes up classes. Click any cell to see the actual conversations, who labeled them, the model's confidence — and relabel, flag for discussion, or undo right there. Confidence control ## You decide how certain the model must be. Set the confidence threshold. High confidence means fewer classifications but almost no errors. Low confidence means more coverage but more uncertainty. You control the tradeoff. High confidence (90%+) Almost never wrong. Classifies 70% of interactions. The rest get flagged for human review. Perfect for compliance-critical models. Balanced (70%+) Good accuracy with broad coverage. Classifies 90% of interactions. Typical production setting for analytics and dashboards. Exploratory (50%+) Maximum coverage, more noise. Classifies 99% of interactions. Use when finding patterns matters more than precision. ## Trust is the foundation. Actions follow. When you trust your models, you can trust everything built on top of them. [Custom Model Training](/platform/model-training) builds the models. Evaluation proves they work. And they power your [Dashboards](/platform/dashboards), [Playbooks](/platform/playbooks), and every solution. ## If you can't explain it, don't deploy it. Full transparency at every level. Your stakeholders see the numbers. Your team sees the errors. Everyone trusts the output. 5 Metrics per class 0 Black boxes 30 d Average integration window ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Your business. Your models. > Train AI models on your own data without writing code. Active learning workflows build custom classifiers from your conversations. Custom Model Training Generic AI models don't understand your business. Labelf lets your domain experts — not data scientists — train models that learn your language, your categories, your edge cases. [Book a Demo ](/book-a-demo)  See it in action ## From description to production in minutes Describe what you want to find. The model starts working. Deploy it. Watch the KPIs move. All in one flow. Three modes ## Start instantly. Improve continuously. Begin with zero-shot classification — no examples needed. As you refine, the model gets sharper. No data science team required at any stage. Active Learning ## Label smarter, not harder. You don't randomly label examples. Labelf analyzes the model's weaknesses and recommends exactly which examples will improve it the most. Label 200 smart examples instead of 2,000 random ones. It finds edge cases, flags your mistakes, balances classes, discovers rare patterns — so your model learns what matters, fast. Training Advisor ## The system tells you what to fix next. Prioritized actions: which classes need more data, which confuse each other, which are star performers. With level progression so you always know how close you are to the next quality tier. No data science required ## The people who know the business train the models. QA analysts, team leads, process developers — the people who actually understand your customers. They click buttons, not write code. Labelf handles the AI. QA Analyst Labels examples at 200-400/hr. Finds patterns in conversations. Validates model output. Team Lead Defines categories based on how the team actually works. Sets edge case rules. Reviews model accuracy. Process Developer Designs the classification hierarchy. Connects models to dashboards. Builds reporting workflows. Labelf Handles model architecture, training, deployment, scaling, and active learning. Zero infrastructure for you. Team performance ## Know who labels well — and who needs help. See every labeler's volume, pace, agreement rate, corrections received, and accuracy trend over time. ## From description to production in minutes. Traditional ML projects take months. With Labelf, you describe what you want, the model starts working, and you refine it with your team until it's production-ready. The platform handles everything else. * Describe → Classify instantly Write what you're looking for in plain language. Zero-shot classification starts immediately. * Label → Refine with Active Learning The system recommends what to label next. 200 smart examples beat 2,000 random ones. * Deploy → Run on everything One click deploys the model. Every new interaction is classified automatically. Feeds dashboards, alerts, and playbooks. Unlimited depth ## Go as deep as you need. Build unlimited hierarchies within a single dimension. Each level trains a dedicated model on just the subset that matters. The deeper you go, the more specific the intelligence gets. Customer Interest 3 levels deep · trained from conversations L1 Streaming 22% of customers L2 Sports 14% L3 Hockey 78% convert on TV Premium L3 F1 65% convert L3 Football 41% convert Skiing · Tennis · ... L2 Fantasy & Sci-fi 5% Documentaries · Kids · ... L1 Summer house 8% Travel · Remote work · Gaming · ... Full conversations or single utterances "Was the customer retained?" needs the full conversation. "Is this a billing question?" needs one message. Choose per model. Include surrounding context when needed. Contact Reason 3 levels deep · root cause drill-down L1 Invoice $42K/mo L2 Perceived wrong information $28K/mo L3 Wrong price $12K/mo L3 Wrong date $9K/mo L3 Wrong services $5K/mo Product mismatch · VAT · ... L2 Missing invoice $8K/mo Payment failed · Refund request · ... L1 Service $31K/mo Order · Cancellation · ... Each level is its own model Level 1 runs on all interactions. Level 2 trains only on "Invoice Issues" — a focused model that's better because it's specialized. Level 3 goes deeper still. Each model is simple. The hierarchy creates the depth. Bucketeering ## Cross everything against everything. Every model you train creates a new dimension. Every dimension gets crossed against every KPI — CSAT, AHT, churn, sales, resolution, cost, transfer rate. Thousands of buckets. In those buckets, you find the patterns that no one could see before. Dimensions × KPIs 2,000+ categories × 18 KPIs = 36,000+ data points Your dimensions ProductRoot CauseCustomer NeedSentimentAgentTeamResolutionSales AttemptChurn SignalChannelLanguageTime of Day + every model you add Crossed against CSATAHTChurn RateSales ConversionResolution RateTransfer RateFCRCostVolumeNPS + custom metrics What pops out "Billing disputes about wrong price" has 3× higher churn than "wrong date" Root Cause × Churn Rate · 4,200 interactions Churn risk Agent Sarah resolves router issues in 4 min — average is 12 min Agent × Product × AHT · What does she do differently? Best practice Hockey fans without streaming convert at 78% — but no one is pitching them Customer Interest × Product × Sales Conversion · $240K/yr opportunity Revenue Tuesday evenings: TV app calls spike 340% — firmware v3.2 only Time of Day × Product × Root Cause × Volume · $18K/mo in agent time Process issue Every model adds a dimension Train a new model and it instantly creates new cross-references with every existing KPI and dimension. The more models, the richer the picture. Anomalies surface automatically The system monitors all buckets continuously. When a combination spikes or trends, it flags it. You don't search for problems — they find you. Filter, slice, drill down Every dimension is a filter. Every KPI is a sort. Start broad, narrow down, find the specific bucket that costs you $18K/month. Then fix it. ## Models are the engine. Everything else follows. Every model you train powers the rest of the platform. [AI Search](/platform/ai-search) helps you find the training examples. [Model Evaluation](/platform/model-evaluation) shows you where it's right and wrong. And the models feed directly into [Dashboards](/platform/dashboards), [Playbooks](/platform/playbooks), and every solution. ## Built for domain experts, not data scientists. The people who know your business best should be the ones training your AI. Labelf makes that possible. 400 Labels per hour by domain experts 0 Lines of code required 30 d Average integration window > In an operation of our scale, moving from data noise to decisive action is paramount. Labelf cuts through millions of interactions, providing the clear signals we need. Letting us act faster and with greater confidence on what truly matters, creating a better customer experience.  Nicklas Hellström Head of Customer Operations Business Improvement, Telia Sweden ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # From insight to action. > Turn conversation insights into automatic actions. Alerts, workflows, and playbooks trigger when the right patterns appear. Playbooks & Alerts Insights without actions are just interesting facts. Labelf closes the loop — when the AI finds a pattern, it generates a playbook for agents or a process alert for the team who can fix it. With cost attached. Automatically. [Book a Demo ](/book-a-demo)  Two output types ## Change behavior. Fix systems. ### Playbooks Change how people handle a situation. "If X happens — don't do this — do this instead." Born from real conversation data, cited with examples, and tracked for adherence. Agents Coaches Team Leaders Head of Ops ### Process Alerts Fix the system, not the agent. Bug reports with a price tag — routed to the team who can fix it. Cost-quantified, prioritized, and tracked to resolution. App Team Network IVR/Telephony Logistics Playbook in action ## Cited. Tracked. Measured in dollars. Every playbook comes with real conversation citations showing what works and what doesn't. Custom ML models track whether agents follow it. The cost of non-adherence is calculated automatically. Process Alert in action ## Bug reports with a price tag. When the system is broken, agents can't fix it — but someone can. Process alerts go straight to the owning team with evidence, cost, and a companion playbook for agents in the meantime. The full loop ## Detect. Recommend. Distribute. Measure. Repeat. Other tools stop at insight. Labelf goes all the way — from detecting a pattern to distributing the playbook, training a model to track adherence, and measuring the business impact in dollars. Automatically. AI detects pattern During normal analysis or agent chat Playbook created With real conversation citations Distributed to agents Via Teams, email, or in-app Custom ML tracks adherence Trained from the playbook definition ROI measured in dollars Adherence × cost-per-miss = savings Continuous monitoring New patterns trigger new playbooks ## Only Labelf can measure if people actually follow the playbook. Other tools can distribute playbooks. Only Labelf can train a custom ML model from real conversation data that automatically detects whether agents follow them — and quantifies the cost of non-adherence. Detect → Recommend AI finds patterns and creates playbooks — everyone can do this Distribute → Track Push to agents and measure adherence with custom ML — only Labelf Measure → Prove ROI Adherence rate × cost-per-miss = actual dollars saved — the full loop ## Playbooks connect everything. The [AI Agent](/platform/ai-agent) discovers patterns. Playbooks turn them into actions. [Custom models](/platform/model-training) track adherence. And the results show up in [Dashboards](/platform/dashboards) and every [Solution](/solutions). ## From insight to action. From action to proof. The complete loop. Detect, recommend, distribute, track, measure. No other tool does all five. 78 % Average playbook adherence $34 K Avg monthly impact per playbook 30 d Average integration window ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Your data. Your rules. > Enterprise security with EU data residency and full data ownership. Your customer conversations stay under your control.  Security & Privacy We process sensitive customer conversations for some of the largest companies in the Nordics. Security isn't a feature — it's the **foundation**. EU-hosted, GDPR-compliant, with on-premise deployment for organizations that need full control. [Book a Demo ](/book-a-demo) Deployment ## Cloud, private cloud, or on-premise. You choose. ### Cloud Fast setup, automatic updates, managed infrastructure. Choose your region — EU, US, or other. We handle everything. Choose your data region Automatic updates & scaling Managed backups & monitoring SOC 2 readiness No hardware to manage ### Private Cloud Deployed in your own cloud account — AWS, Azure, or GCP. You own the infrastructure. We manage the application. Your cloud account Your network, your VPC We manage the application Custom scaling policies Full infrastructure visibility ### On-premise Deployed inside your own data center. Full control over every byte. For organizations with the strictest requirements. Your hardware, your network Data never leaves your building Custom GPU allocation Air-gapped option available Kubernetes or Docker Privacy & Compliance ## Built for regulated industries. Banks, telecom, insurance, government — our customers operate in heavily regulated environments. Labelf is designed to meet the strictest requirements from day one. GDPR compliant Full compliance with EU data protection regulation EU AI Act ready Transparent AI with full audit trails and explainability PII anonymization Automatic detection and masking of personal data in conversations Role-based access Granular permissions — who can see, label, train, deploy, and export Full audit trail Every model change, every label, every deployment — logged and traceable Data retention control Set retention periods — auto-delete audio, transcripts, or all data after X days Anonymization ## Personal data detected and masked automatically. Names, phone numbers, addresses, personal IDs, credit card numbers — detected in transcripts and masked before analysis. Configurable whitelists for product names and prices you need to keep. Anonymization example Before "Hi, my name is Erik Lindqvist and I'm calling about my account. My phone number is 073-482 19 55 and I live at Storgatan 14, Stockholm." After "Hi, my name is \[PERSON] and I'm calling about my account. My phone number is \[PHONE] and I live at \[ADDRESS]." Detected: NamesPhone numbersAddressesPersonal IDsCredit cardsEmail addressesBank accounts ## European company. European values. Labelf is a Swedish company. Our team, our code, and our cloud infrastructure are in Europe. We're not subject to the US CLOUD Act or FISA. Your data stays where you put it. Swedish company Incorporated and headquartered in Sweden EU data centers Cloud infrastructure within EU borders only No US jurisdiction Not subject to CLOUD Act, FISA, or Patriot Act Own AI models Trained and deployed on our infrastructure — no third-party API calls with your data ## Security you can prove to your board. Not just promises — full documentation, audit trails, and deployment flexibility that satisfies the most demanding compliance teams. EU Data residency 100 % GDPR compliant 0 Third-party data sharing ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Every call. Every word. > Transcribe every customer call with high accuracy. Turn voice into searchable, analyzable text with speaker separation. Transcription Everything Labelf does with voice data starts with transcription. If the transcript is wrong, every model trained on it is wrong. That's why we built our own — fine-tunable per customer, with speaker separation and industry vocabulary support. [Book a Demo ](/book-a-demo)  See it in action ## High-precision transcription with speaker separation. Call Transcript · 6m 42s · Nov 14, 2024 98.4% accuracy Swedish A Agent Hi, welcome to customer service, you're speaking with Sandra. How can I help you? C Caller Hi, yeah I'm calling about my router. It stopped working since Monday. I have a ProBox 500 and it just keeps blinking red. A Agent I understand, that sounds frustrating. I can see here that you have broadband 500 Mbit with a ProBox 500 router. Have you tried restarting it by unplugging the cable for 30 seconds? C Caller Yeah I've done that like five times now. It doesn't help. My neighbor switched to another provider and has no issues at all, so I'm starting to wonder if I should do the same. Detected in this transcript Competitor mention Product: ProBox 500 Broadband 500 Mbit Troubleshooting attempted What makes it different ## Not a generic model. Yours. Generic transcription models don't know your product names, your brand terms, or your industry jargon. Labelf's transcription can be fine-tuned to learn your specific vocabulary — "ProBox 500" instead of "pro box five hundred", "StreamPlus" instead of "the streaming add-on". Speaker separation Agent and caller identified and labeled automatically 100+ languages Swedish, Norwegian, Danish, German, English, Arabic, and more Custom vocabulary Fine-tune for your product names, brand terms, and industry jargon PII anonymization Personal data detected and masked in the transcript automatically Batch & real-time Process historical recordings in batch or transcribe live calls Audio retention control Keep the transcript, delete the audio — or delete both after X days Vocabulary fine-tuning ## Teach it your language. Every industry has jargon. Every company has product names. Generic models get them wrong. Labelf learns yours. Before vs After fine-tuning pro box five hundred ProBox 500 Product name broad band five hundred megabit Broadband 500 Mbit Service sim card SIM card Technical term my broadband app MyBroadband App name the streaming add on StreamPlus add-on Product name Continuous improvement ## Correct it once. It learns forever. When you spot a transcription error, fix it right in the conversation view. Those corrections feed back into the model — it learns from every edit and gets better over time. Word Error Rate is calculated automatically so you always know exactly how accurate it is. ### Edit in conversation view See a wrong word? Click it, fix it, done. No export, no separate tool. Corrections happen right where you're already working — in the conversation browser. ### Model retrains on corrections Every correction becomes training data. The transcription model learns your vocabulary, your accents, your audio environment. It gets better with every edit your team makes. ### WER tracked automatically Word Error Rate is calculated from your corrections. Watch it drop over time as the model learns. Know exactly how accurate your transcriptions are — not a guess, a measurement. ## Bad transcription poisons everything. Every model you train reads the transcript. If "ProBox 500" is transcribed as "pro box five hundred", your product classification model learns the wrong thing. If "StreamPlus" becomes "the streaming add-on", your search can't find it. Transcription quality is the foundation of everything. Transcription Wrong transcript = wrong training data = wrong model = wrong insights = wrong actions Search Can't search for "TeliaVox" if it was transcribed as "tele vox" — semantic search helps but exact terms matter Classification Models learn from what they read — garbage in, garbage out. High-precision transcription is non-negotiable ## Every word matters. We get them right. High-precision transcription is the foundation. Everything else — classification, search, coaching, playbooks — depends on getting the words right. 100 + Languages supported 98 % Accuracy after fine-tuning 30 d Average integration window ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Unlock your Insights > Transparent pricing for AI interaction analytics. From mid-market to enterprise — choose the plan that fits your volume and needs. Pricing TL;DR ## Choose the plan that fits your business needs.  ### Business Suite For medium sized customer interaction operations ready to deploy quickly in the cloud  ### Enterprise Suite Built for the highest flexibility regarding deployment and integrations for larger enterprises with sophisticated needs #### Business [Get started](/book-a-demo) #### Enterprise [Get started](/book-a-demo) Labelf engine Custom AI Models for Interaction classification   Hierarchies for classification   Tailored for your business lingo and dynamics   Custom prompting and summaries of interactions   Sentiment Analysis   100+ Interaction Languages supported   Real-time Analytics Issue spike detection and analysis   Root cause analysis   LLM for customer interaction analytics   AI-powered reporting & insights   Fully customizable real-time dashboards   Support and Success Help center   Support portal   Success Workshops  Standard SLA   Custom SLA  Dedicated Success Manager  Product Roadmap Roundtables  Interaction Types Tickets   Chats   Emails   Customer Surveys   Voice Calls   Voice call transcription   Omnichannel connectivity Telia Ace   Zendesk   Genesys   Salesforce   ServiceNow   Trustpilot   Custom CX Integrations  Data Warehouse integration  API enrichment of interaction data  Privacy & Security User role & permission management   Customizable anonymization   Customizable data retention   SSO SAML  Deployment Cloud   Private Cloud  On premise  [Get started](/book-a-demo) [Get started](/book-a-demo) ## Frequently Asked Questions How long does it take to get started with Labelf? Most deployments are live within 30 days. You connect your data sources, train your first models, and start seeing insights immediately. No data science team required. Do I need a data science team to use Labelf? No. Labelf is designed for domain experts — QA analysts, team leads, process developers. You describe what you want to find in plain language, and the AI builds the model. No code, no ML expertise needed. What languages does Labelf support? Labelf supports 100+ languages for both transcription and analysis. You can search in one language and find results in another — the semantic understanding transfers across languages. Can Labelf be deployed on-premise? Yes. Labelf offers cloud, private cloud, and full on-premise deployment. Your data never has to leave your infrastructure. We are EU-based with no US jurisdiction. How does pricing work? Pricing is based on conversation volume and deployment model. Both Business and Enterprise plans include unlimited users and models. Contact us for a custom quote based on your specific needs. What data sources can Labelf connect to? Labelf integrates with 90+ tools including CRM systems, ticketing platforms, call center software, BI tools, and data lakes. We also offer a full REST API for custom integrations. --- # From quantified insight to prioritized action. > How a global D2C sportswear retailer used Labelf to classify support tickets, reduce handling times, and uncover product improvement opportunities. Customer Case [Book a Demo ](/book-a-demo) The challenge A global D2C sportswear brand selling across 100+ markets knew from external sources like Trustpilot that customer experience needed improvement — but lacked the **depth of insight to know what to fix first**, and the **data quality to trust the numbers**. The result 90%+ AI accuracy after just hours of training 80%+ automation of sub-categories within two months 5 min to install via standard Zendesk integration The problem ## Data they couldn't trust. Agents were manually tagging every closed ticket — a process that was both time-consuming and notoriously imprecise. The categorization hierarchy needed for real insight was too complex for agents to apply consistently, leading to significant differences between teams. The result: they were burdening agents with a task that produced data they couldn't trust enough to make strategic decisions. Ineffective reporting Manual, time-consuming, and insufficient for decisions — no automation or necessary detail level. Missing deep analysis No cluster analysis of reviews, no correlation between ticket types and satisfaction scores. Unclear CSAT picture No unified view of customer satisfaction linked to specific ticket types or root causes. Static dashboards No interactivity for deep dives, trend analysis, or filtering by date, market, or order number. The solution ## From simple tags to hierarchical intelligence. The technical implementation was straightforward — Labelf's standard Zendesk integration was installed in about five minutes. After installation, a scoped training dataset was imported for the AI models, defined in close collaboration with Labelf's implementation consultant. The company could now leave their previous flat structure behind and establish a fine-grained, hierarchical categorization for deeper insights. * Bootstrapped from existing data The first model was trained on agents' historical tagging — then Labelf's quality control surfaced suspected errors for efficient correction. * 90%+ accuracy in hours Just a few hours of data quality review was enough for the first model to reach production-grade accuracy. * Confidence-based automation Sub-category models used confidence thresholds — only auto-tagging when certain, and routing uncertain tickets to agents for continuous training. * Self-improving over time Uncertain cases became training data. Sub-category models reached 80%+ automation within two months — improving continuously. What was enabled ## Insight they could finally trust. For the first time, the company had a detailed, unified, and current picture of what customers are reaching out about — and how each category impacts key KPIs. ### Cross-functional reporting Trends and insights across markets and products — enabling faster, more precise actions across the entire organization, with measurable follow-up on the actual impact. ### Market comparison With a distributed support team speaking different languages, a cross-market overview was previously impossible. They quickly identified that product returns were driving volume and lower satisfaction specifically in one market — and could compare root causes across geographies. ### Reliable data for decisions Interactive dashboards with trustworthy data — including cluster analysis of product reviews — that enabled better decisions aimed at improving customer satisfaction, response time, and handling time. ## Stop burdening agents with work the AI should do. Manual categorization was consuming agent time while producing data nobody trusted. With Labelf, the same categorization is done automatically, at higher accuracy, with a hierarchical depth that was previously impossible to maintain. The models start working from day one and get smarter over time. Uncertain predictions become training data. Within two months, what started as a handful of manually trained sub-categories reached 80%+ automation. From unreliable tags to trusted intelligence. ## Quantified Insight. Prioritized Action. Higher accuracy, deeper categorization, cross-market visibility, and trusted data — transforming customer service from a cost center into a strategic intelligence source. 90 %+ AI accuracy in hours 80 %+ Sub-category automation in 2 months 30 d Average integration window ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Scaling multilingual support across 2,000% growth. > How a global ride-hailing service used Labelf to analyze driver and rider feedback at scale, improving safety scores and operational efficiency. Customer Case [Book a Demo ](/book-a-demo) The challenge A fast-growing ride-hailing service expanding across multiple markets needed to **route tickets to the right agents**, **prioritize critical incidents**, and handle a growing number of languages — without slowing down. The result Days from kickoff to live AI-powered ticket routing 100+ languages handled — fully language-agnostic 2,000% growth in daily trips since 2020 — supported by Labelf "Labelf AI Team is fantastic! They are really, really helpful and patient in setting up the AI platform to match our needs. Highly recommended for others who need AI assistance in categorizing tickets!" Head of Customer Support Systems Global Ride-Hailing Service Background ## Rapid growth, global complexity. With daily trips growing over 2,000% since 2020 and expansion into new markets, the customer support team needed to scale fast — without losing control over quality or response times. Route to the right agent Automatically assign tickets based on content so specialized agents handle the right issues. Prioritize critical incidents Flag vehicle incidents and safety issues for immediate handling — no manual triage. Handle any language Operate across multiple markets with regional languages and dialects — without separate models per language. The solution ## Live in days, not months. The implementation was led by the company's support systems lead in close collaboration with a Labelf implementation consultant. Together they built a categorization structure that optimized routing logic — reusing existing categories and adding a handful of new ones in Labelf's annotation environment. The work was completed in just a few days, after which the models were progressively activated in the live support environment. * Standard integration Connected to their existing ticketing system via Labelf's standard integration — no custom development needed. * Automatic routing rules Incoming tickets are automatically routed to specific queues based on content. Priority incidents are flagged instantly. * Flexible model structure Easy to add, merge, or restructure categories as the business evolves — no downtime, no starting over. What was enabled ## Efficiency that scales with growth. From competence-based teams to compliance-ready incident handling — all powered by AI categorization. ### Competence-based teams Agents now work in skill-based groups. Tickets are automatically routed to the team with the right expertise, significantly reducing handling time. ### Seamless market expansion As the company expanded into new markets, the increased ticket volume and regional language complexity were handled without adding operational overhead. Labelf's language-agnostic models scaled effortlessly. ### Compliance-ready incident handling An unexpected strategic win: categorization models now instantly identify and prioritize traffic incidents with potential police involvement. These critical cases are immediately escalated to the compliance team — previously they risked getting stuck in the backlog. ## Built for change, not just for today. A key benefit the company has valued over the years is Labelf's flexibility. Categories can be added, merged, or restructured at any time — without starting over or requiring downtime. What started as ticket routing became the foundation for compliance, quality assurance, and market expansion — all from the same AI categorization models. One platform. Every market. Every language. ## From Startup to Global Scale. Faster response times, smarter routing, and regulatory compliance across every market — powered by AI that understands any language. 100 + Languages supported 0 Custom integrations needed 30 d Average integration window ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # From weeks to hours: data noise to actionable insights. > How a leading Scandinavian telecom reduced churn and improved agent performance by analyzing millions of customer conversations with Labelf. Customer Case [Book a Demo ](/book-a-demo) The challenge With hundreds of thousands of customer interactions per month, the simple questions had already been handled. What remained were **increasingly complex and deep-seated problems**, leading to increases in both time spent and cost per case. The result 500 fewer calls per week from a single process fix 2M+ SEK annual savings from one identified issue Hours instead of weeks to identify customer trends "In an operation of our size, it is crucial to move from data noise to decisive actions. Labelf sorts millions of interactions and gives us the clear signals we need. This allows us to act faster and with greater confidence on what truly matters, creating a better customer experience." Head of Customer Operations Business Improvement Leading Scandinavian Telecom Background ## When the easy wins were already taken. Historically, they had succeeded in pressing down costs and raising customer satisfaction through continuous improvements. But they needed the next step — deeper insights into **why** customers contact them, not just **that** they do. Improve the customer meeting See which calls succeed, where coaching has great effect, and create personal development for employees. Cut costs and prioritize Move from reactive to quickly identifying root causes and acting on what yields the greatest business value. Evaluate GenAI/LLM Understand intent, topic, and context in every customer dialogue — replacing inefficient legacy technology. Concrete example ## From frustration to "plug and play" By analyzing customer calls, a complicated login process for customers with new digital TV boxes was identified as creating major frustration and a call volume of hundreds of cases every week. By changing the process and sending out pre-logged-in boxes, the number of calls decreased by **500 per week**, corresponding to an annual saving of over **2 million SEK** and a significantly improved start to the customer journey. 500 fewer calls / week 2M+ SEK saved annually A new era of customer understanding ## Insights that flow to the people who can act. The journey began with a test where thousands of calls could be transcribed and categorized in real-time. Every call was analyzed with high precision, giving a detailed understanding of customers' needs, challenges, and wishes. Instead of valuable feedback stopping at customer service, it now flows directly to the people who can act. Product Owners See exactly which products and services create frustration or joy and make data-driven decisions for adaptation. Process Owners Immediately identify bottlenecks and inefficient moments in the customer journey — inside and outside customer service. Marketing Deeper understanding of customers' drivers and needs for more accurate campaigns that fulfill expressed needs. What was enabled ## Real-time intelligence, real impact. From coaching metrics that agents can actually influence, to trend identification in hours instead of weeks. ### Fair and effective coaching Instead of only measuring "hit-rate" (sales per call), they now measure "offer-rate" — a fair metric employees can influence. Team leaders give specific coaching and see the effect the very next day. ### Immediate trend identification Analyses that previously took weeks now happen in hours. Both acute spikes and smaller recurring problems are captured — making it possible to quickly quantify the size, impact, and cost of any issue. ### Smarter quality assurance Inefficiencies like unnecessary transfers were automatically identified, while the calls that best exemplify high customer satisfaction were surfaced — providing insights to lower AHT and strengthen QA with data-driven examples. ## Technology is only half the journey. Real value cannot be created by new technology alone, but by how it is integrated into business processes. The biggest gains come from the ability to systematically identify and fix dozens of smaller problems that were previously invisible. Interaction analysis is no longer a project — it is a fundamental capability that makes operations faster, smarter, and more customer-centric. And this is only the beginning. ## From Data Noise to Decisive Action. Reduced operating costs, increased customer satisfaction, and the identification of new revenue opportunities through deeper customer insights. 500 Fewer calls per week from one fix 2 M+ SEK annual savings 30 d Average integration window ## We're ready, are you? [Book a Demo ](/book-a-demo) --- # Build on Labelf. > REST API for text classification, multi-model inference, and text similarity. Classify any text in 5 minutes. API Documentation https\://api.labelf.ai/v2 REST + JSON Bearer Auth Labelf exposes a comprehensive REST API for classification, model management, text similarity, dashboards, and data pipelines. Everything the platform does, the API does — classify a single utterance, score every interaction in a contact center, or feed real-time churn signals into your CRM. <50ms Avg latency 100+ Languages 99.9% Uptime SLA 8 Texts per request ## Classification Levels Customer interactions are not flat. A single phone call contains dozens of utterances, belongs to an errand that may span multiple channels, and sits within the full history of a customer relationship. Labelf classifies at every level — giving you the resolution you need, from individual statements to organization-wide patterns. U Utterance level Classify individual messages or statements within a conversation. Identify the moment a customer mentions churn, a competitor, or a specific pain point — even inside a 40-minute call transcript. C Conversation level Classify the entire call, chat session, or email thread. Determine the overall intent, outcome, and sentiment of each interaction as a whole. E Errand / Ticket level Classify the full customer case across multiple interactions. A billing dispute that starts in chat, moves to phone, and resolves via email is one errand — Labelf sees it as one. P Customer level Aggregate classifications across all interactions for a single customer. Surface patterns like repeated cancellation attempts, escalating frustration, or emerging cross-sell signals over time. A Agent level Analyze agent behavior patterns across all their conversations. Identify coaching opportunities, measure retention-save techniques, and surface which agents excel at turning detractors into promoters. The same `/inference` endpoint handles all levels. The difference is what text you pass — a single utterance, a full transcript, or a concatenated case history. Your models learn from whichever level you train on. ## Language Support The same endpoint handles 100+ languages without configuration. No language parameter is needed — detection is automatic. Train a model on English examples and it generalizes across languages. Or train on mixed-language data from your actual contact center. Request — Swedish ``` { "texts": ["Jag vill säga upp mitt abonnemang"] } ``` Response ``` { "predictions": [ { "label": "Cancellation", "confidence": 0.92 } ], "text_language": "sv", "processing_time_ms": 41 } ``` Labels are always returned in the language they were defined in during training. A Swedish customer saying *"Jag vill säga upp mitt abonnemang"* returns the same `Cancellation` label as an English customer saying *"I want to cancel my subscription."* ## Quick Start Classify your first text in under 5 minutes. Get an API key from your Labelf dashboard, then: curl Python Node.js Copy ``` curl -X POST https://api.labelf.ai/v2/models/42/inference \ -H "Authorization: Bearer $LABELF_API_KEY" \ -H "Content-Type: application/json" \ -d '{"texts": ["I want to cancel my subscription"]}' ``` ``` import requests response = requests.post( "https://api.labelf.ai/v2/models/42/inference", headers={"Authorization": f"Bearer {API_KEY}"}, json={"texts": ["I want to cancel my subscription"]} ) print(response.json()) ``` ``` const res = await fetch("https://api.labelf.ai/v2/models/42/inference", { method: "POST", headers: { "Authorization": `Bearer ${process.env.LABELF_API_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ texts: ["I want to cancel my subscription"] }), }); const data = await res.json(); ``` Response: ``` { "predictions": [ { "label": "Cancellation", "confidence": 0.94 }, { "label": "Billing Issue", "confidence": 0.03 }, { "label": "Technical Support", "confidence": 0.02 } ], "model_id": 42, "text_language": "en", "processing_time_ms": 38 } ``` ## Next Steps [Authentication →](/resources/developers/authentication) [Generate API keys, understand workspace roles, and set up Bearer tokens](/resources/developers/authentication) [Classification →](/resources/developers/classification) [Full reference for the inference endpoint — multi-language, confidence thresholds, hierarchical models](/resources/developers/classification) [Text Similarity →](/resources/developers/similarity) [Compare sets of texts by meaning — score conversations against known examples, find duplicates, build training sets](/resources/developers/similarity) [Models →](/resources/developers/models) [List, inspect, and manage your deployed models — versions, performance metrics, and training status](/resources/developers/models) [Multi-Model →](/resources/developers/multi-model) [Run hierarchical classification in a single request — product, issue type, and root cause in one call](/resources/developers/multi-model) [Errors & Limits →](/resources/developers/errors) [HTTP status codes, rate limiting, and troubleshooting common integration issues](/resources/developers/errors) --- # Authentication > Generate API keys, configure Bearer tokens, and set up OAuth2 for service-to-service integrations. API Documentation https\://api.labelf.ai/v2 REST + JSON Bearer Auth All API requests require authentication. Labelf uses Bearer tokens scoped to a workspace, so every request is tied to a specific team and its permissions. Generate API credentials from your workspace settings in the Labelf dashboard. ``` Authorization: Bearer lbl_sk_7f3a9b2c... ``` ## How to Get a Key Four steps, under a minute: 1 Open Workspace Settings In your Labelf dashboard, click the gear icon in the sidebar. 2 Navigate to API Credentials Under the workspace settings menu, select **API Credentials**. 3 Create API Key Click **Create API Key**. Give it a descriptive name (e.g. "CRM Integration Prod"). 4 Copy and Store Securely Copy the key immediately — it is only shown once. Store it in your secrets manager or environment variables. ## Token Types Labelf supports two authentication mechanisms. Choose based on your integration pattern. BEARER API Key Static key prefixed with `lbl_sk_`. Best for server-to-server integrations, batch pipelines, and scripts. No expiry — rotate manually. ``` -H "Authorization: Bearer lbl_sk_7f3a..." ``` OAUTH2 JWT Token Short-lived JWT tokens issued via OAuth2 client credentials flow (backed by Ory Hydra). Best for micro-service architectures and environments that require token rotation and audit trails. ``` -H "Authorization: Bearer eyJhbGci..." ``` ## Workspace Roles API keys inherit the permissions of the workspace role they are created under. Assign the minimum role needed for each integration. | Role | Classify | Train Models | Manage Data | Manage Users | | ------------------ | -------- | ------------ | ----------- | ------------- | | `api-user` | Yes | — | — | — | | `labeler` | Yes | — | Label only | — | | `viewer` | Yes | — | Read only | — | | `dashboard_editor` | Yes | — | Dashboards | — | | `member` | Yes | Yes | Yes | — | | `admin` | Yes | Yes | Yes | Yes | | `owner` | Yes | Yes | Yes | Yes + Billing | For production inference pipelines, use the `api-user` role. It can classify text but cannot modify models, datasets, or workspace settings — the principle of least privilege. ## Key Properties Scoped per workspace Each key is tied to a workspace and inherits its role-based permissions. One workspace can have multiple keys for different services. Rotate without downtime Create a new key before revoking the old one. Both remain active during the transition window. Zero-downtime rotation. Audit trail Every API call is logged with the key identity. Track which integration made which request for compliance and debugging. ## On-Premise Deployments For on-premise and private cloud deployments, the entire authentication stack runs within your infrastructure. API keys are issued by your local Labelf instance, tokens never leave your network, and there is no dependency on external identity providers. Self-contained auth The on-prem Labelf instance manages its own Hydra instance, user database, and key storage. No outbound auth calls. SAML / OIDC federation Integrate with your existing identity provider (Azure AD, Okta, etc.) via SAML 2.0 or OpenID Connect for single sign-on. ## Security Labelf is built for regulated industries — telecom, banking, insurance — where customer data is sensitive and compliance is not optional. EU data residency All cloud data is stored and processed within the EU. No data crosses EU borders unless you explicitly configure an on-prem deployment elsewhere. GDPR compliant Data processing agreements, right-to-deletion support, and purpose limitation built into the platform. We are the processor; you are the controller. PII handling Optional PII anonymization at ingestion. Classify the intent without storing the personal data. Configurable per workspace. TLS everywhere All API traffic is encrypted with TLS 1.2+. No plaintext endpoints. Certificate pinning available for on-prem. Rate limiting Per-key rate limits prevent abuse and protect shared infrastructure. Limits are configurable for enterprise plans. On-prem isolation For maximum control, deploy Labelf entirely within your own infrastructure. Air-gapped deployments supported. For a complete overview of Labelf's security posture, see the [Security page](/platform/security). ## Example Request A simple authenticated request to list your models: ``` curl -X GET https://api.labelf.ai/v2/models \ -H "Authorization: Bearer $LABELF_API_KEY" ``` Response: ``` { "models": [ { "id": 42, "name": "Contact Reason - Mobile", "label_count": 24, "training_samples": 12847, "status": "deployed" }, { "id": 43, "name": "Root Cause - Billing", "label_count": 18, "training_samples": 8421, "status": "deployed" } ] } ``` [← Overview ](/resources/developers)[Classification →](/resources/developers/classification) --- # Classification > Classify text against trained models. Returns labels with confidence scores. Up to 8 texts per request. API Documentation https\://api.labelf.ai/v2 REST + JSON Bearer Auth POST `/v2/models/{model_id}/inference` Classify one or more texts against a deployed model. Returns labels sorted by confidence. Up to 8 texts per request, any language, sub-50ms typical latency. This is the core endpoint that powers everything from real-time call tagging to overnight batch classification of millions of interactions. ## Request Body | Parameter | Type | Description | | ----------------- | --------- | --------------------------------------------------------------------------------------------------------------------------------------------- | | `texts` | string\[] | Required Texts to classify. Max 8 per request. Each text can be up to 10,000 characters. | | `max_predictions` | integer | Max labels to return per text. Omit to return all labels with their scores. Set to 1 for top-match only. | | `label_filter` | string\[] | Only return scores for these specific labels. Useful when you only care about a subset of the model's labels (e.g. churn-related categories). | ## Response Returns an array of predictions for each input text, sorted by confidence descending. Each prediction includes the label name and a score between 0 and 1. The response also includes the detected language and processing time. | Field | Type | Description | | -------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------ | | `predictions` | object\[]\[] | Outer array maps 1:1 to input texts. Inner array contains label/confidence pairs, sorted by confidence descending. | | `model_id` | integer | The model used for classification. | | `text_language` | string | Detected ISO 639-1 language code of the input text. | | `processing_time_ms` | integer | Server-side processing time in milliseconds. Typically 20–50ms. | ## Classification Levels The same endpoint classifies at any level of granularity. The difference is what you pass as text — a single customer message, a full conversation transcript, or an aggregated case history. ### Utterance Level Classify a single customer message. This is the highest-resolution classification — identify the exact moment a customer mentions a specific issue. Request ``` curl -X POST https://api.labelf.ai/v2/models/42/inference \ -H "Authorization: Bearer $LABELF_API_KEY" \ -H "Content-Type: application/json" \ -d '{"texts": ["I can'\''t log in to my account"]}' ``` Response ``` { "predictions": [[ { "label": "Login Issue", "confidence": 0.96 }, { "label": "Account Access", "confidence": 0.02 }, { "label": "Password Reset", "confidence": 0.01 } ]], "model_id": 42, "text_language": "en", "processing_time_ms": 31 } ``` ### Conversation Level Pass the entire conversation transcript as a single text. The model classifies the overall intent and outcome of the interaction. Request — full transcript ``` { "texts": [ "Agent: Thank you for calling, how can I help?\nCustomer: Hi, I've been having issues with my broadband for a week now. It keeps dropping out every evening.\nAgent: I'm sorry to hear that. Let me check your connection...\nCustomer: I've already called twice about this. If it's not fixed I'm switching to another provider.\nAgent: I understand your frustration. Let me escalate this to our network team..." ], "max_predictions": 3 } ``` Response ``` { "predictions": [[ { "label": "Technical Issue - Broadband", "confidence": 0.91 }, { "label": "Churn Risk", "confidence": 0.87 }, { "label": "Repeat Contact", "confidence": 0.79 } ]], "model_id": 42, "text_language": "en", "processing_time_ms": 44 } ``` ### Using max\_predictions Control how many labels are returned per text. Set to `1` when you only need the top match for routing. Omit to get the full confidence distribution across all labels — useful for analytics and dashboard visualization. ### Using label\_filter When you only care about specific outcomes, use `label_filter` to restrict the response. This is common in churn monitoring: you want to know *if* a conversation is about cancellation, not what else it might be about. Request — filter to churn-related labels ``` { "texts": ["I want to switch to a competitor"], "label_filter": ["Cancellation", "Churn Risk", "Competitor Mention"] } ``` Response ``` { "predictions": [[ { "label": "Competitor Mention", "confidence": 0.93 }, { "label": "Churn Risk", "confidence": 0.88 }, { "label": "Cancellation", "confidence": 0.71 } ]], "model_id": 42, "text_language": "en", "processing_time_ms": 29 } ``` ## Hierarchical Classification Real contact center analytics requires multiple classification dimensions. A single interaction often needs to be tagged with a product, an issue type, and a root cause. Labelf handles this by running multiple specialized models in sequence — or in parallel via the [Multi-Model endpoint](/resources/developers/multi-model). A typical hierarchy for a telecom: 1 Product Model Mobile, Broadband, TV, Fixed Line, Bundled 2 Issue Type Model Billing, Technical, Delivery, Cancellation, Upgrade 3 Root Cause Model Wrong charge, Payment failed, Invoice unclear, Coverage issue, Speed complaint Each model is trained independently on its own label set. You can call them individually or use the [multi-model endpoint](/resources/developers/multi-model) to run all three in a single request: Multi-model request — single call, three models ``` POST /v2/multi-model/inference { "model_ids": [42, 43, 44], "texts": ["I was charged twice for my mobile bill last month"], "max_predictions": 1 } ``` Response ``` { "results": { "42": { "predictions": [[{ "label": "Mobile", "confidence": 0.97 }]] }, "43": { "predictions": [[{ "label": "Billing", "confidence": 0.95 }]] }, "44": { "predictions": [[{ "label": "Wrong charge", "confidence": 0.91 }]] } }, "processing_time_ms": 52 } ``` ## Confidence Thresholds Every prediction includes a confidence score between 0 and 1. How you use that score depends on the cost of getting it wrong. Here is a typical strategy used by enterprise customers: \>0.9 High confidence Auto-tag and route. Feed directly into your CRM, ticketing system, or real-time dashboard. No human review needed. 0.7–0.9 Medium confidence Tag but flag for review. The classification is likely correct, but a human should verify before triggering high-stakes actions (e.g. retention offers). <0.7 Low confidence Route to a human for manual classification. These uncertain predictions become training data — this is how customers bootstrap and continuously improve their models. This pattern creates a virtuous cycle: the model handles the easy cases, humans handle the edge cases, and the edge cases become training data that makes the model better. Over time, the low-confidence bucket shrinks as the model learns from your specific data. ## Multi-Language Classification Labelf's models are multilingual by default. The same endpoint handles 100+ languages — no language parameter needed, no separate models per language. Train on English, classify in Swedish, Arabic, or Japanese. Labels are always returned in the language they were defined during training. Swedish ``` { "texts": ["Jag vill säga upp mitt abonnemang"] } ``` Response ``` { "label": "Cancellation", "confidence": 0.92 } "text_language": "sv" ``` Arabic ``` { "texts": ["أريد إلغاء اشتراكي"] } ``` Response ``` { "label": "Cancellation", "confidence": 0.89 } "text_language": "ar" ``` Japanese ``` { "texts": ["サブスクリプションを解約したいです"] } ``` Response ``` { "label": "Cancellation", "confidence": 0.87 } "text_language": "ja" ``` All three requests return the same English label `Cancellation` despite being in different languages. No language parameter, no per-language model, no configuration. This is particularly valuable for multinational operators or contact centers that handle multiple markets from a single platform. ## Full Example Classify two texts, return top 3 predictions ``` POST /v2/models/42/inference Authorization: Bearer your-api-key Content-Type: application/json { "texts": [ "I want to cancel my subscription", "How do I update my payment method?" ], "max_predictions": 3 } ``` Response ``` { "predictions": [ [ { "label": "Cancellation", "confidence": 0.94 }, { "label": "Billing Issue", "confidence": 0.03 }, { "label": "Technical Support", "confidence": 0.02 } ], [ { "label": "Billing Issue", "confidence": 0.91 }, { "label": "Account Management", "confidence": 0.05 }, { "label": "Technical Support", "confidence": 0.02 } ] ], "model_id": 42, "text_language": "en", "processing_time_ms": 38 } ``` [← Authentication ](/resources/developers/authentication)[Multi-Model →](/resources/developers/multi-model) --- # Dashboards & Analytics > Dashboard and analytics API — charts, metrics, SQL queries, and embeddable dashboards. API Documentation https\://api.labelf.ai/v2 REST + JSON Bearer Auth The full analytics engine — custom charts, metrics, SQL queries, an AI analytics agent, and embeddable dashboards. Turn classified interaction data into decisions your team acts on today. ## Dashboard Types Different roles need different views. A contact center manager checking queue health at 9 AM needs different data than a VP reviewing quarterly churn metrics with the board. ### Operational Real-time KPIs for contact center managers — live call volume, queue depth, churn alerts firing right now, CSAT trend over the last 4 hours. Refreshes every 60 seconds. ### Analytical Trends over time for team leads and analysts — contact reason shifts week-over-week, seasonal patterns, the impact of a pricing change on complaint volume. Built for weekly and monthly reviews. ### Executive Summary metrics for leadership — churn saved this quarter, upsell revenue attributed to agent recommendations, NPS by segment. One screen, no drill-down required. ### Embedded White-labeled dashboards embedded in your own tools via iframe. Guest access tokens, theme-matched to your brand, no Labelf branding visible. Your team sees insights without leaving their existing workflow. ## Chart Types Every chart pulls from your classified datasets. Drag-and-drop builder for non-technical users, raw SQL for analysts who want full control. | Chart | What it shows | | ------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Volume over time | Track interaction counts by day, week, or month — broken down by category, channel, or team. Spot spikes before they become incidents. | | Category breakdown | Pie or stacked bar showing the distribution of contact reasons, churn signals, or sentiment across your customer base. | | Agent comparison heatmap | Compare agent performance across categories — who handles billing complaints best, who converts upsell opportunities, where coaching is needed. | | Root cause tree | Hierarchical drilldown from top-level categories to specific sub-issues. "Billing" breaks into "Invoice confusion", "Payment failed", "Price increase objection". | | CSAT correlation | Scatter plots and trend lines showing how classification categories correlate with satisfaction scores. Find which issues drive low CSAT. | | AHT by category | Average handle time segmented by contact reason. Identify which topics take longest and where knowledge base improvements would save the most time. | ## SQL Engine Every classification, every metadata field, every timestamp is queryable via standard SQL. Analysts write custom queries against their classified data — no ETL pipeline, no data engineering team required. Results render as tables or charts directly in the dashboard. ``` SELECT classification AS contact_reason, COUNT(*) AS volume, AVG(handle_time_sec) AS avg_aht, AVG(csat_score) AS avg_csat FROM classified_interactions WHERE created_at >= '2025-10-01' AND channel = 'phone' GROUP BY classification ORDER BY volume DESC ``` This query answers: "What are our top phone contact reasons this quarter, and how do they correlate with handle time and satisfaction?" — a question that typically requires a data team and a week of work. ## AI Analytics Agent Ask questions in plain language. The AI agent translates your question into SQL, runs it against your classified data, and returns a visualization with an explanation. No SQL knowledge required. "What drove the call spike last Tuesday?" The agent identifies that Tuesday's volume was 2.3x the daily average, driven by a 340% increase in "Service outage" classifications concentrated in the Nordics region between 08:00-12:00 — and presents this as a time-series chart with the anomaly highlighted. "Which agents have the highest upsell conversion on fiber plans?" Returns a ranked bar chart of agents by upsell success rate on fiber-tagged interactions, with handle time comparison — so you can see whether top performers are also faster or if they invest more time per conversation. ## Embeddable Dashboards Generate guest access tokens and embed dashboards via iframe in your own internal tools, customer portals, or executive reporting pages. Dashboards inherit your brand theming — colors, typography, logo — so they look native to your platform. ```