Data stage
Best for
Regulated or specialist domains where only an expert can judge what a good answer is.
How it runs
Work with your subject-matter experts to turn their judgement into scoring criteria.
Annotation guidelinesDefinitions of a good answer, with worked examples reviewers can apply consistently.
Run a pilot batch and measure agreement between reviewers. Rewrite the guidelines until it's high.
Calibrated reviewer poolDomain reviewers whose agreement scores meet the agreed threshold.
Produce data at scale, with a senior reviewer settling disagreements and spot-checking quality.
Instruction and preference datasetsPrompt-response pairs and ranked comparisons, each carrying agreement scores.
Train with DPO, or with a reward model plus RL, then re-score against the rubric.
Aligned checkpointReady for evaluation against your release criteria.
In practice
Numbers from the base model versus the model we shipped.
Generic models produced fluent summaries that omitted or invented findings, so clinicians rewrote most of them.
Clinician acceptance
Before: 58%After: 86%
Unsupported findings per 100 notes
Before: 7.1After: 0.9
FAQ
Yes, and for highly specialised domains that's often best. We provide the tooling, training, and quality checks, and your experts supply the judgement.
In moderation and with human review, yes. We use it to fill rare edge cases, never as the whole dataset, and we test on real data only.
Often paired with
Share a sample of your data and a target metric. We'll come back with a plan and a timeline.
We reply within one business day (IST).
