Your model passes the demo but stalls at production sign-off, because the standard for what a domain expert calls OK or NG does not exist as data. Gappy builds it. Vetted domain operators grade your real AI outputs, and you get three things you can ship with: an eval set you run in CI, a guardrail spec, and RAG-ready knowledge.
Vetted domain operators grade what your AI actually said. About 15 minutes per task, with a full audit trail.
Pull real answers and hard cases from your vertical AI feature into judgment tasks. No synthetic data.
A vetted operator marks each output OK or NG, records the reason, and logs the exception. Who judged what, when, and why is captured.
An eval set you run in CI, a guardrail spec, and RAG-ready knowledge. You keep all of it.
One judgment, up close
The question is no longer whether the model can do it. Frontier models cleared that. The question is whether you can prove it is safe enough to ship where a wrong answer is costly. Without a standard for what a domain expert calls OK or NG, there is no eval set, no guardrails, and no sign-off.
The blocker is not the model. It is the missing data: no agreed standard, captured as data, for what a domain expert calls OK or NG.
Six capabilities. Each one leaves you with something you keep.
Domain operators sorted by field, role, years, and case type. Vetted practitioners, not crowd labelers.
Turn real AI answers and edge cases from your product into gradable tasks in minutes.
Experts mark each output OK or NG with the reason and the exception. About 15 minutes per task.
Who judged what, when, and why. A record you can show customers, auditors, and regulators.
Download results as an eval set for CI, a guardrail spec, and RAG-ready knowledge.
Show exactly what an expert reviewed and approved. Scoped supervision, no name-lending.
Labeling giants and expert marketplaces serve the supply side (labs building models). Eval tools serve the harness. Nobody sells expert-graded, vertical-specific, reusable ground-truth data to the demand side: the teams shipping products. That gap is the category.
Engineers plus hired experts build one-off eval sets. Six figures, not reusable, slow. The real status quo.
A repeatable workflow that returns an eval set, a guardrail spec, and RAG knowledge you keep and rerun.
Scale, but noisy on specialized domains. You get labels, not eval or guardrail artifacts, built for model training.
Vetted domain operators. The output is production-certification artifacts, not raw labels.
Closest analog, but the buyer is the lab, billing is hourly cost-plus, and no reusable data asset is left in your pipeline.
Built for the team shipping the product. Every judgment leaves a reusable asset in your pipeline.
The harness to run evals, not the expert ground truth to put in them. A complement, not a substitute.
We fill the data side: the expert ground truth your eval harness runs on.
Judgment in a meeting. Not reusable, not auditable.
Judgment captured as structured data with a full audit trail.
I shipped vertical AI into production and hit this exact wall at the certification gate. The model was good enough. What I could not produce was the proof: an eval set and guardrails grounded in what a domain expert calls OK or NG. That data did not exist, so I had to build it by hand. Gappy is that process, turned into a product. We built it and won the OpenAI Business Hackathon 2026.
Our buyer is the team shipping a product, not a frontier lab. Mercor, Surge, and Scale sell expert data to labs training base models. We sell to teams trying to get a product into production. That is the whitespace.
Bring 100 real outputs from a vertical AI feature you are trying to ship. In two weeks we return a graded eval set, a guardrail spec, and a 30-minute readout, hand-built with a domain expert in your field. Free. You keep the eval set.
Gappy turns expert judgment into the data that certifies vertical AI for production. Vetted domain operators grade your real AI outputs OK or NG, with the reason and exception logged, and you get an eval set for CI, a guardrail spec, and RAG-ready knowledge, with a full audit trail of who judged what, when, and why.
Teams shipping vertical AI into production in specialized, high-stakes domains: financial, travel, legal, healthcare, insurance, and tax. This covers vertical AI startups, internal AI teams at enterprises, and dev shops building vertical AI for clients. Your demo works, but you cannot get sign-off because the standard for what a domain expert calls OK or NG does not exist as data.
Those serve the supply side. Labeling vendors and expert marketplaces sell data to labs training base models, billed per label or hourly, and leave no reusable asset in your pipeline. We serve the demand side: teams shipping a product. Every judgment fills your private eval set, and you keep it.
You keep your eval set, guardrail spec, and knowledge outright. Gappy keeps an anonymized cross-customer rubric and exception taxonomy. Your raw and identifiable data never leaves your set.
Five teams get an eval set hand-built free before anyone else. Bring 100 real outputs, and in two weeks you get a graded eval set, a guardrail spec, and a 30-minute readout. You shape the product and keep all your data.
A platform fee plus per-judgment task fees. We will validate exact numbers with design partners. The benchmark we beat: expert-grading one eval set in-house runs $100k+ and is not reusable.
5 design partner slots. 100 judgments. 2 weeks. Free. You keep the eval set.
Become a design partner