Human evaluation for AI models

Find out where a model fails before your users do, with raters who explain their scores.

What AI Model Evaluation needs

Automatic metrics miss what people notice: tone, factual slips, unsafe replies, a regression in one language. Human evaluation catches them if it is designed well.

We build test sets from your use case, qualify raters on your rubric, run blind comparisons and report results by slice, with the raw judgements and agreement statistics.

Data types

Text
Speech
Image
Video

Industries

Technology and software
Banking and finance
Healthcare
Telecom
Automotive
Retail and e-commerce

The data it takes

Custom test sets
Prompts, audio or images that mirror production, including the hard cases.
Scorecards
Per-criterion scores with rater IDs and rubric version.
Side-by-side comparisons
Blind preference between two systems or versions.
Error taxonomies
Failures tagged by type and severity.
Regression reports
What changed between two versions, with the items that moved.

How we collect it

01
Built from your traffic
Test cases come from your use case and failure logs, not a generic benchmark.
02
Raters calibrated
Trained on your rubric and checked against a gold set.
03
Blind and randomised
Outputs are shown without labels and in random order.
04
Reported by slice
Results cut by language, topic and difficulty.