// cognitive_dataops
Know where your model fails before production does.
Datafy Lab builds expert-reviewed evaluation sets, failure taxonomies, preference data, and reasoning traces so you can measure model quality on the cases that matter — not just aggregate accuracy.
// who_it_is_for
Build an eval set when…
- You only measure aggregate accuracy and miss failure modes.
- You need a benchmark that reflects real deployment conditions.
- You’re collecting preference data for fine-tuning or RLHF.
- Your evals don’t catch the edge cases that break production.
eval_deliverables.json
01Expert-reviewed evaluation set
02Model failure taxonomy
03Preference & ranking data
04Reasoning trace annotations
05Scenario coverage report
06Benchmark & scoring rubric
// what_we_produce
Evaluation data that drives the loop.
// 01
Evaluation sets
Curated, expert-reviewed test cases that mirror real deployment conditions.
// 02
Failure taxonomies
A structured map of how and where your model breaks.
// 03
Preference data
Human preference and ranking data for fine-tuning and alignment.
// 04
Reasoning traces
Step-by-step annotations that expose where reasoning goes wrong.
// faq
Common questions
Curated, expert-reviewed test cases, failure taxonomies, preference data, and reasoning traces used to measure model quality on the scenarios that matter — not just aggregate accuracy.
Standard benchmarks measure average performance. Our evaluation sets target your model's real failure modes and deployment conditions, so the score reflects production risk, not leaderboard rank.
Yes. We produce human preference, ranking, and reasoning-trace data for fine-tuning, RLHF, and alignment work, with QA and consistency reporting.
Each failure category becomes a prioritized data target — what to capture, annotate, and certify next — so evaluation drives the rest of the foundry.
Each qualified dataset ships with a Model-Ready Data Certificate covering rights, privacy, coverage, balance, annotation quality, limitations, and readiness.
Book a Data Failure Audit. An evaluation set and failure taxonomy are usually the first deliverables.