Coding-agent evaluation and training data
What we mean by verified
Every coder clears two tiers before they see a study: identity and credibility, then an independent skills assessment matched to your task.
What teams use verified coders for
The work spans evaluation and training. Each study is scoped to your requirements and delivered by engineers who have passed independent assessment in the languages your task needs.
What gets captured
When a verified engineer works through a coding task, the submission is the working session as a whole rather than a single diff. We record the decision path: the approach they started with, the point where they abandoned it, each tool call and its result, test runs and their failures, retries, timing, and the edits in between. The final pull request is one row of signal. The session behind it is hundreds.
How it is structured
Each session resolves into a sequence of steps a model can learn from: the state the engineer saw, the action they took, and the outcome it produced, with enough context to reconstruct why. We can also capture the engineer's own rationale at key decision points, which turns a raw log into graded reasoning data.
What it unlocks
Process-level data supports work that outcome grading cannot: process reward modeling, where good intermediate decisions earn signal alongside good endings; agentic evaluation, where a run is judged against how a strong engineer would have proceeded; and behavioral cloning, where models train on how experts actually work.
Quality that holds at scale
Verification happens before the work: identity checks, credibility signals, and an independent skills assessment matched to your requirements. Review happens on every submission: behavioral cheat detection covers copy-paste, tab switching, plagiarism, and ChatGPT matching, with AI use blocked or permitted per task depending on your design.
