Your business. Your models.
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.
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.
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.
Describe what you want to classify in plain language. The model starts working immediately based on your instructions alone.
Write detailed instructions with edge cases, test against a validation set, and iterate on the prompt until it performs well.
Label examples with Active Learning assistance. The model trains on your data using LoRA/SetFit and deploys as a dedicated classifier.
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.
You labeled this "Service cancellation" but the model is 94% sure it's "Product inquiry"
"What happens if I cancel my add-on? Do I lose the discount on my main plan?"
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.
It has 52% less data than the average (72 vs 151). The model is biased towards larger classes.
When the answer was "Cancellation", the model guessed "Service change" 14 times. The boundary between these classes is unclear.
Reliable accuracy (100%). 294 samples at Level 13. No actions needed.
81 samples with 100% accuracy. Just 17 more examples would push it to the next confidence level.
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.
Labels examples at 200-400/hr. Finds patterns in conversations. Validates model output.
Defines categories based on how the team actually works. Sets edge case rules. Reviews model accuracy.
Designs the classification hierarchy. Connects models to dashboards. Builds reporting workflows.
Handles model architecture, training, deployment, scaling, and active learning. Zero infrastructure for you.
Know who labels well — and who needs help.
See every labeler's volume, pace, agreement rate, corrections received, and accuracy trend over time.
| Labeler | Labels | Pace | Agreement | Corrections | Skipped | Trend |
|---|---|---|---|---|---|---|
SK Sandra K. Top performer | 4,612 | 320/hr | 94% | 12 | 45 | |
FL Frida L. Consistent | 3,847 | 290/hr | 91% | 18 | 67 | |
JE Johan E. Needs review | 1,203 | 85/hr | 76% | 47 | 23 | |
AD Andreas D. Good | 2,156 | 210/hr | 88% | 24 | 89 |
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.
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.
"Was the customer retained?" needs the full conversation. "Is this a billing question?" needs one message. Choose per model. Include surrounding context when needed.
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.
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.
Train a new model and it instantly creates new cross-references with every existing KPI and dimension. The more models, the richer the picture.
The system monitors all buckets continuously. When a combination spikes or trends, it flags it. You don't search for problems — they find you.
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 helps you find the training examples. Model Evaluation shows you where it's right and wrong. And the models feed directly into Dashboards, 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.
Labels per hour by domain experts
Lines of code required
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.
