Why we built RepoWise

AI reads your code. It does not understand your codebase.

We started building RepoWise because we kept watching AI coding tools make the same wrong assumptions about our projects. The tools could find functions, trace imports, and suggest completions. But they had no idea why we chose PostgreSQL over DynamoDB. They did not know our naming conventions. They could not tell that the payments module was being deprecated next quarter.

This kind of knowledge lives in places code search cannot reach. Architecture decisions made in Slack threads six months ago. Business rules that one senior engineer carries in their head. Naming conventions the team agreed on during a standup but never wrote down.

The frustration of starting from scratch

Every new AI tool means a blank slate. You spend an afternoon writing a .cursorrules file. It works for a week. Then three PRs merge and the file describes a codebase that no longer exists. You update it. Two weeks later, the same thing happens.

Now try using Claude Code alongside Cursor. You need a CLAUDE.md too. If someone on the team uses Copilot, that is a third file. Three context files, three formats, all going stale at different rates. Nobody maintains them because the effort outweighs the payoff.

New engineers feel this the most. They join a team, open an AI tool, and get suggestions that violate every team convention. They ask questions the AI should answer but cannot, because the context was never captured. Senior engineers become the human context layer, answering the same questions over and over.

What we tried first

We tried the manual approach. We wrote detailed context files by hand. We documented architecture decisions. We created coding standards guides. It worked for about a week.

The problem is not writing the files. The problem is keeping them accurate. Code moves fast. A refactor changes the data model. A new service gets added. A dependency gets replaced. The context file does not know about any of it. Within one sprint, the file describes a codebase from the past.

The insight that changed our approach

Context generation is an engineering problem, not a documentation task. You do not need writers. You need a system that learns your code, asks the right questions, validates the output, and keeps everything in sync as the code evolves.

Reading code can reveal architecture, data models, and structure. But it cannot capture why you built things the way you did. For that, you need to talk to the people who made those decisions. The combination of automatic analysis and short, targeted interviews produces context that neither approach achieves alone.

What we built

RepoWise is a suite of four products that together solve the context problem from every angle:

Together they form a context layer that sits between your codebase and every AI tool your team uses. Set it up once. After the first scan, your context refreshes automatically as the code changes.

Your code stays yours

Your source is processed in memory and discarded within a day. Only the context we generate persists, fully isolated from every other customer and encrypted end-to-end. We do not store source code on our servers, and you can wipe everything with one request.

Try it

Two commands and every AI tool you use understands your codebase: