Turn human judgment into continual learning
Tuor gives AI teams a real-time review workspace where production outputs, human corrections, and user feedback become structured data for evals, training, and quality monitoring.
Teams spend human effort correcting outputs, but edits stay trapped in product UIs, spreadsheets, and support queues.
A basic review UI is easy. Routing, audit history, structured capture, and downstream integration are the hard parts.
Without structured feedback, teams cannot see recurring mistakes or know where the model needs attention.
Feedback waits on ad hoc exports and cleanup before it can become evals, training data, or product insight.
Route production model outputs to reviewers as they happen, instead of batching feedback later.
Show reviewers the model input, output, and context they need to make high-quality judgments.
Turn every approval, edit, rejection, and note into structured feedback data at review time.
Send reviewed traces to evals and training workflows through APIs, webhooks, or bulk exports.
Turn user flags, edits, and thumbs-downs into reviewed corrections for evals and training workflows.
Sample live traffic to catch regressions, drift, and edge cases before they become support tickets.
Require review before outputs reach users when accuracy requirements are high and mistakes are costly.
Route red-team traces and safety signals to reviewers to catch jailbreaks, prompt injection, policy violations, and PII leaks.