Tell us what your model needs to do better

You describe the task and what your model gets wrong. We author the rubric with your team, staff the experts, and run a pilot batch through every quality layer before anything scales. The pilot is the proposal.

Specialist work is done by experts in our internal network, every one of them interviewed by our AI interviewer and calibrated on your rubric before production. High-volume labeling runs through partnered networks in China, Korea and Vietnam under the same six layers.

It is processed to produce the deliverable you asked for and retained for the engagement plus the archival window we agree. It is never used to train models and never shared beyond the judge providers named in your engagement. This is in our terms, not just on this page.

Six composed layers, each reporting what it found. Agreement is broken down per label and per annotator with item counts beside every coefficient. Every LLM judge is calibrated against human adjudication per batch. Every rubric is versioned, and earlier batches are re-scored when it changes.

Yes, and the delivery is built for it. Every figure resolves to the labels, annotators and rubric clause behind it. Inputs are content-addressed and rubric versions are pinned, so a rerun six months later returns the same numbers or names exactly what changed.

Illustrative samples of a training record, a feedback and rubric record, and an evaluation report are above. A real sample on your task is what the pilot produces.