Tuor

Continual Learning Infrastructure for AI Teams

Turn human feedback into structured training signals. Capture corrections from experts and users, and feed them back into your AI pipeline for continuous improvement.

What Tuor does

Capture corrections from experts and end users

A purpose-built interface for internal experts to review model outputs, plus integrations to ingest end-user feedback and convert it into structured annotations.

Prioritize the highest-value review work

Surfaces the highest-value items first. Traces with more errors, lower confidence scores, or higher business impact are reviewed before the rest.

Understand failure modes

Gives model owners, product teams, and leadership visibility into approval rates, error categories, reviewer disagreement, and failure patterns.

Integrate feedback into evals and training

API-first architecture for sending traces to reviewers and feeding corrections into your pipeline via webhooks, enabling real-time downstream use.

How it works

  1. Connect your AI pipeline Integrate with a few lines of code. Tuor captures model inputs and outputs without changing your existing workflow.
  2. Collect and route feedback Define rules to route traces to internal experts by confidence score, business impact, sampling rate, or custom logic — and ingest end-user feedback from your product.
  3. Review and correct Experts approve, reject, edit, or tag traces. Every decision becomes a structured training signal attached to the trace.
  4. Sync results Completed reviews are delivered to your app or pipeline in real time via webhooks.
  5. Close the loop Turn reviewed traces into reusable data for evals, fine-tuning, and system iteration.

Use cases

Human-gated deployment

Verify every output before it reaches end users in high-stakes environments like medical coding, data extraction, and loan underwriting.

Feedback loops

Capture negative user signals to trigger review and generate fine-tuning data.

Quality assurance

Continuously monitor model health by auditing a sample of production traffic.

Safety review

Review traces from red-teaming attacks to identify jailbreaks, prompt injections, and PII leakage.