Reduce risk. Improve AI quality.

Clone human judgment into automated quality assurance across every request, prompt, and model change.

HUMAN ALIGNMENT

Evals aligned to your standards

Turn reviewer standards into repeatable quality checks for prompts, models, and production traffic.

Human in the loop at scale

Apply human judgment to every request and ensure your evals are aligned with your human experts before they go live.

Support Response Quality eval with sampled test cases beside human and automated rubric scores for Faithfulness, Groundedness, and Helpfulness, plus a 96 percent agreement overlay from three reviewers

Evals on a visual canvas.

Drag judges, checks, and rules into place. Tune prompts with your team, then run the same eval across models.

Eval canvas

Support Response Quality

Eval canvas for Support Response Quality showing a node graph that connects model output to LLM judges and a rubric aggregator

Catch regressions before users do.

Track quality in real time and get alerted when outputs start failing in production.

Live eval results table for sampled production requests with user, score, pass or fail status, and failing criteria such as Groundedness, showing an 82 percent pass rate

Fix what's failing.

Ship faster and address issues as they happen with your coding agent through MCP.

Cursor IDE chat where a coding agent pulls failing eval results through MCP, reports Groundedness regressions, and inserts a system prompt fix in support-agent.ts

One backend
for better AI