For large codebases
Fix a bug without drowning the model in the whole repo
Discover the area that owns the bug, and load only that subsystem into your AI over MCP.
The problem
In a big repo, loading everything into an AI wastes the context window and dilutes the answer. The model needs the one subsystem the bug lives in, not the other forty.
How it works
- 01
Find the area
Areas clusters the repo into its real subsystems by imports and shared terms.
- 02
Load just that
Serve the area to your AI client over MCP, or export it as a focused bundle.
- 03
Fix with focus
The model reasons over the relevant code only, so the fix is sharper and the context stays cheap.
Result. Targeted context, sharper fixes, and a context window you are not wasting.
Keep going
More you can do
Onboard a new developer in days, not weeks
Hand a new hire a guided tour of the codebase their AI assistant actually understands.
Turn a Confluence space into a NotebookLM briefing
Pull a whole space into clean Markdown, then let NotebookLM turn it into an audio overview or a deck.
Get an AI review that sees the whole picture
Bundle your git changes, their tests, and what depends on them, behind a review prompt.
Make sense of a pile of spreadsheets and invoices
Turn XLSX, PDF, and Word files into clean Markdown tables an AI can actually read.