Trust what you deploy.
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.
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.
See exactly which classes the model confuses, at what confidence levels, and click into specific examples to understand why. Adjust the confidence threshold to trade precision for recall.
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.
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.
| Actual ↓ Predicted → | Billing dispute | Technical issue | Cancellation | Product inquiry | Sales opp. |
|---|---|---|---|---|---|
| Billing dispute | 412 | 3 | 2 | 8 | 1 |
| Technical issue | 5 | 589 | 4 | 12 | 6 |
| Cancellation | 1 | 2 | 274 | 3 | 0 |
| Product inquiry | 6 | 8 | 1 | 122 | 19 |
| Sales opp. | 2 | 4 | 0 | 14 | 70 |
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.
Almost never wrong. Classifies 70% of interactions. The rest get flagged for human review. Perfect for compliance-critical models.
Good accuracy with broad coverage. Classifies 90% of interactions. Typical production setting for analytics and dashboards.
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 builds the models. Evaluation proves they work. And they power your Dashboards, 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.
Metrics per class
Black boxes
Average integration window
