Process

Initial engagement

We start with a working session to pin down your research goals, target behaviors, and integration constraints. From there, we co-design a lab-specific schema and difficulty bands, share representative samples from our existing library (terminal/CLI, Python completion, bug-fix), and plan a custom dataset calibrated to your baseline agents and timelines.

Exclusivity

We create lab-specific datasets under NDA with explicit IP assignment and optional time-bound or perpetual exclusivity. Content is never resold. Exclusivity is enforced through contributor contracts: all authors are senior engineers working under NDAs and work-for-hire/assignment agreements, so your team retains exclusive access per contract.

Multi-layered Quality Assurance Process

Comprehensive Automated Checks

Our internal systems automatically run and check to see if the problem passes all basic checks i.e. Not generated by AI / Fulfills the problem target requirements of a 10-30% pass ratio for the agent.

Cohesive human evaluation loops

All problems are overseen and go through a multi step approval process by at least 2 Senior Engineers / ML Specialists within our internal revisions division, to ensure data diversity, problem fairness and quality delivery.