Frontier AI data & evaluation
Turning
ambiguity into
intelligence.
We build the benchmarks, training data, and evaluation workflows that show frontier AI teams what their models can actually do.
Built for
forward-thinking AI teams
- AI Labs
- Frontier Model Teams
- Enterprises
- Researchers
- Engineering Teams
The measurement gap
Benchmarks are broken.
Frontier teams ship models against scores that no longer mean what they used to. We build evaluations grounded in the messy, ambiguous work models are actually asked to do.
01 / Saturated
Scores top out before models do.
Public benchmarks hit their ceiling within months. A 95% headline tells you little about what the next model generation will break on.
02 / Contaminated
Test sets leak into training data.
Once a benchmark is on the open web, it ends up in pre-training corpora. Models recall answers instead of reasoning toward them.
03 / Disconnected
Puzzles are not production.
Real work is ambiguous, multi-turn, and full of unstated constraints. Most evals test clean, single-shot tasks that rarely happen in practice.
What we do
Benchmarks, data, and the workflows between.
One team, one quality bar. Use the benchmarks to find where your model breaks, the data to fix it, and the workflows to prove it stayed fixed.
Internal quality gate
Four gates. Every dataset. No exceptions.
A benchmark is only as trustworthy as its labels. Every item we ship, whether it's an eval task or a training record, passes the same four checks.
- 01
Blind Re-Review
A second expert independently re-evaluates every item without seeing the first reviewer's scores, removing single-reviewer bias.
- 02
Consistency Audit
Automated checks flag scoring outliers, incomplete rubric fields, and prompt-response mismatches across the batch.
- 03
Senior Calibration
A senior technical lead samples every batch to keep rubric interpretation consistent across reviewers and domains.
- 04
Final Sign-Off
Nothing ships until it clears all gates. Every record carries a provenance trail back to its source pull request.
- Quality gates on every dataset
- 4
- Quality gates on every dataset
- Independent blind expert reviews per item
- 2×
- Independent blind expert reviews per item
- Records traced to their source pull request
- 100%
- Records traced to their source pull request
- Records shipped without senior sign-off
- 0
- Records shipped without senior sign-off
Process
From signal to training-ready.
A four-stage engagement that starts with your model's gaps, not our catalogue.
Discover
Map your model's gaps, define target behaviors, and agree on the signals your pipeline needs.
Source
Extract signal from production repositories: pull requests, review cycles, and engineering decisions.
Evaluate
Experts write, solve, and score every item, then run it through all four quality gates.
Deliver
Provenance-tracked datasets and eval suites, formatted for your training and evaluation stack.
The quality of your evaluation is the ceiling on your model. Everything else is noise.
Start with a pilot
Find out what your model can actually do.
Tell us where your model is uncertain. We'll scope a pilot benchmark or dataset around it, usually within a week of our first call.