Built in production, on frontier AI lab training programs.
The review stack comes from two years of automated review at scale. Client and program names are withheld; ask us on a call.
- 17languages scored by one auto-review system
- 1,000+annotators reviewed
- 20+concurrent projects on one deployment
- ~10xfaster review turnaround
Five systems, one discipline.
LLM auto-reviewers for training programs
Scored 1,000+ annotators' work with specific feedback, before it reached human review.
Central deployment for prompts and reviewers
A guideline change made once reaches every project that needs it.
Reasoning evaluation for an open-weights model
Calibration and QA, plus tooling that automated the repetitive parts of review.
Manual review steps automated
Humans kept on the work that needs judgment.
Bayesian experimentation in production
Before AI: decide what you measure, measure it properly, trust it only when it holds up.
Every layer answers a problem we hit.
Single scores weren't actionable
So quality is split into dimensions, each with a reason.
One judge was too noisy
So three reviewers vote, and two are needed to fail.
Reading a task proves nothing
So every task is run: reference passes, empty fails.
The costliest faults hide in scoring
So repair starts with verifiers, proven by re-running.
Guidelines change everywhere at once
So rubrics live in one place and deploy to every project.
Automation has to earn trust
So every agent is calibrated against expert judgment.
Send us 20 tasks. We'll show you which ones are broken, and why.
A free 20-task pilot. Book a 20-minute call to start.