Human judgment, built in.
A model learns from the answers people give it, and it is judged by the test they write. Philotic’s own labeling team writes both to one standard, with the people who do the work making the hard calls, and your experts teaching the model from their daily workflow.
The hard calls go to people who make them for a living.
Labels you can build a model on.
Domain experts on the hard calls
One standard per domain
Test sets you can trust
Your experts teach the model
Confidential by design
From guide to frozen test.
- GuideThe domain’s written standard, with worked examples of the hard cases.
- LabelAnnotators answer each item against the guide.
- Expert callAnything uncertain or high-stakes goes to a domain expert, who makes the final call.
- AgreementReviewer agreement is measured on every batch. A batch that falls short is reviewed again before it is used.
- FreezeTest items are reviewed and frozen, so the evaluation set never moves under a model.
The difference is who decides.
Compared with typical crowd labeling.
| Typical crowd labeling | Philotic | |
|---|---|---|
| Who makes the hard calls | Whoever picks up the task | Domain experts who do the job for a living |
| The standard | Instructions written per task | A written guide per domain, with reviewer agreement measured on every batch |
| Test sets | Labeled like everything else | Reviewed and frozen before any model is judged on them |
| Your experts | Outside the loop | Labeling from their own workflow, and the labels are yours |
| Your data | Sent to an outside workforce | Seen only by your people, or by our vetted team with your written approval |
Your experts label as they work.
In Philotic Desktop a model proposes, and a person confirms, corrects or rejects it from the keyboard. Every answer is a label you own. Try answering one.
Philotic Desktop with an illustrative workspace and sample data; not a customer.
Put your experts in the loop.
Tell us which calls your people make every day. We’ll write the standard with them, label a first batch, and show you the test set your models will be judged on.