Leaner AI. Less context, lower AI bills.

It's how we set up AI so each task reads only the information it needs. We organize your business knowledge into a clear structure, then give every task a short list of what to load and what to skip. Less to read means smaller bills and fewer wrong answers.

AI models bill by the token, and every file a task reads costs tokens. When a task loads the three files it needs instead of your whole knowledge base, every run is smaller. Across hundreds of runs a day, that adds up.

ICM, the Interpretable Context Methodology, is a published method from Jake Van Clief and David McDermott that uses folder structure as the architecture for AI work. Each stage has a short contract: what it reads, what it does and what it hands off. It's open source under the MIT license.

Yes. The structure is plain files, so it works with whichever model you use, including private AI running on hardware in your office. Small, focused context is also what lets a local model handle real work.

It depends on how much your AI reads today, so we won't quote a number before we've seen your setup. On the free audit call we look at what your tasks load and show you where the waste is.