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We're building a compression layer that turns raw codebases into semantic context — so AI models see structure, not noise.
AI models drown in raw code
When you feed an entire codebase to a language model, most of the context window is wasted on boilerplate, syntax noise, and duplicated structure. The model works harder, costs more, and understands less.
Most of a codebase is boilerplate, imports, and syntax noise — tokens the model doesn't need as raw text.
We extract the meaningful structure and feed it as compact chunks — same meaning, far fewer tokens.
Structured context with parent pointers helps models resolve dependencies instead of guessing from flat files.
Parse. Chunk. Compress.
The Compression Engine sits in your editor. It reads your code, extracts the semantic units that matter, and feeds them to the model as structured context.
Tree-sitter parses your code into an abstract syntax tree — language-aware, not regex hacks.
Functions, classes, methods, and globals are extracted as individual chunks with parent pointers.
The model receives structured semantic units instead of raw files — same meaning, fraction of the tokens.
Built for developers, by developers
We're starting with a VS Code extension. More integrations and a hosted API are on the roadmap.
Get early access
Be the first to try the Compression Engine when we launch.