Engineering · Codebase intelligence

A codebase that explains itself

We turned a sprawling, multi-service codebase into a living graph anyone can question in plain language. Every answer cites the exact file and line.

Code graphSemantic clusteringGrounded Q&ADeveloper tooling
The challenge

The people who most needed to understand the codebase, product leads, engineering managers, new hires, could not read it, and the people who could were constantly interrupted to explain it. Every planning conversation started with a scavenger hunt.

What we built
  • A graph built from real signal: how code calls itself, what changes together, how the frontend connects to the backend.
  • Pre-computed feature clusters, so the structure is found before a model is ever involved, not hallucinated by one.
  • A question-and-answer layer that returns the exact file, line, and owner, with the same answer for a product lead and a senior engineer.
The outcome
  • New hires ship in days instead of weeks.
  • Answers are specific and verifiable, not opinions, so nobody has to second-guess them.
  • Senior engineers stop being a lookup service for the rest of the team.
FAQ

Common questions

We build the structure first. A graph captures how code calls itself, what changes together, and how the frontend connects to the backend, and feature clusters are pre-computed. The model answers over that real structure instead of guessing it, which is why answers cite an exact file and line.

The graph is rebuilt from real signal in the source, so it tracks the code rather than a stale snapshot. Because every answer points to a specific file, line, and owner, a wrong or outdated answer is verifiable on the spot rather than taken on faith.

Yes. The approach is built for sprawl: it spans services and follows calls across boundaries, including frontend-to-backend connections. The bigger and less legible the codebase, the more value there is in a graph that makes its structure explicit and questionable in plain language.

We start by building the graph and clusters over your real code, then layer question-and-answer on top. Teams usually see new hires onboarding in days instead of weeks once the system is live, with senior engineers freed from being a constant lookup service.

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