From insight to action.
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.
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.
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.
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.
"Billing disputes where the customer mentions a competitor offer have 3× higher churn than other billing disputes. Agents who offer a price-match within the first 2 minutes retain 72% of these customers."
Price-match on competitor mention
Customer calls about a billing dispute and mentions a competitor's pricing or offer during the conversation.
Don't dismiss the competitor mention or redirect to the standard retention script. Don't say "I understand" and then read the generic offer. The customer has done research — they need to feel heard, not processed.
Acknowledge the competitor offer specifically. Ask what attracted them to it. Then offer a price-match or bundle within the first 2 minutes — before they've mentally committed to switching. Use: "I can see why that's attractive. Let me see what I can do for you right now."
"I saw they have the same speed for 299 kr. You're charging me 449."
"Let me check what I can do. I see you've been with us for 4 years — I can match that price and add 3 months free."
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.
TV app crash on firmware v3.2 — Samsung 2023 models
The TV app freezes and crashes when users switch channels on Samsung 2023 TVs running firmware v3.2. Started Monday after OTA update. Customers describe "black screen", "freezing", "app stops working" — all the same root cause.
"Every time I switch from SVT to TV4 the whole thing goes black. I have to unplug the box."
Caller · Nov 11 · Samsung QE55 · FW v3.2"It started doing this Monday. Was fine before. Now it crashes three or four times every evening."
Caller · Nov 12 · Samsung QE65 · FW v3.2While the app team fixes the firmware, here's what agents should do:
If customer reports TV app freezing → check firmware version first. If v3.2 on Samsung 2023 → confirm known issue, offer manual downgrade instructions, log as firmware bug.
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.
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.
Playbooks connect everything.
The AI Agent discovers patterns. Playbooks turn them into actions. Custom models track adherence. And the results show up in Dashboards and every Solution.
From insight to action. From action to proof.
The complete loop. Detect, recommend, distribute, track, measure. No other tool does all five.
Average playbook adherence
Avg monthly impact per playbook
Average integration window
