Our Methodology
Current RL environments test whether a model can edit code and pass a test. Our environments test whether a model can operate as an enterprise software engineer — navigating requirements, making architectural decisions, and delivering within organizational constraints.
01 — The Gap
Existing RL environments do a great job of training models on coding tasks. We take it further — adding the full enterprise context so models learn to operate as software engineers within real organizational constraints.
02 — The 8 Layers
A real enterprise project is not just code. It's requirements, debates, decisions, constraints, reviews, and organizational reality. Our environments capture all of it.
03 — Anatomy
04 — Multi-Skill Verification
Because the environment contains the full project lifecycle, we can set the task entry point at any stage — and verify the model's output against what actually matters at that stage.
| Task Type | What the Model Does | Verification |
|---|---|---|
| Requirements → Design | Given PRD + team discussions, produce a technical design document | All requirements addressed, architecture is sound, edge cases covered |
| Design → Stories | Given design doc, break into epics and stories with acceptance criteria | Completeness, dependency ordering, story sizing, no gaps |
| Stories → Implementation | Given stories + existing codebase, implement the feature | Tests pass, code review standards met, no regression, compliance checks |
| Code → Review | Given a PR with planted issues, provide thorough review | Catches bugs, security issues, style violations, suggests improvements |
| Incident → Fix | Given an incident report + codebase, find root cause and fix | Fix resolves the issue, no new problems, includes post-mortem |
| Migration → Delivery | Given legacy system + target spec, plan and execute migration | Functional parity, backward compatibility, no data loss, performance maintained |
| Full SDLC | Given just the PRD, produce everything through to working code | Multi-stage verification at each phase of delivery |
05 — Our Approach
We don't imagine what enterprise environments should look like. We take real projects we've delivered, strip all identifying and sensitive information, and what remains becomes the environment. The artifacts are authentic because they came from reality.
Pick a completed enterprise engagement — a migration, integration, or platform build that represents a common pattern.
Catalog every artifact: PRDs, design docs, Jira exports, discussion threads, code, reviews, test plans, runbooks.
Strip all PII, PHI, client names, proprietary business data. Replace with synthetic equivalents that preserve the shape and complexity.
Abstract client-specific details into industry patterns. "Acme Bank" instead of the real name, synthetic data with the same structure.
Build Docker environment, write verification scripts for each SDLC stage, validate that the environment tests what matters.
06 — Our Differentiation
Years of hands-on enterprise implementation gives us an intuitive understanding of how these projects actually unfold — the change management processes, the DBA pushback, the compliance requirements. That lived experience shapes every environment we build.
Our environments are derived from real project patterns, not imagined from scratch. The messy discussion thread where someone says "this won't work because the batch job locks the table" — that texture comes from having been there.
We go beyond testing whether code passes a unit test. Our verification spans the entire delivery lifecycle — requirements understanding, architectural decisions, code quality, compliance, and operational readiness.
Each environment teaches models how enterprises actually work, which makes them better at helping enterprises — which is exactly what AI labs want to offer their customers. Better environments lead to better models lead to stronger demand.