of enterprise CIOs are actively throttling or governing AI usage as token costs climb.
LLM calls per request in agentic workflows, each one multiplying the same token assumptions.
Every available tool and its schema gets sent along, whether the request needs it or not.
The same code and documents get re-sent turn after turn instead of reused.
Bloated context takes longer to process, slowing down every response.
Oversized requests burn through rate limits faster, stalling development work.
The context that actually matters gets buried in everything that doesn’t.
Proprietary code and documents travel to the cloud more often than they need to.
Semantic search across your entire codebase and document repository.
Strips out everything that AI doesn’t need.
Zero latency – runs entirely on local CPU.
Secured. No cloud infrastructure required.
Locates the exact code and documents your AI needs, out of your entire knowledge base.
Chooses only the tools and metadata that a request actually needs.
Cuts unnecessary content down to precise, high-value context.
Keeps proprietary code and documents on-premises, by design.
Ranks context by true relevance before it ever reaches the model.
Clear, ongoing visibility into what every request actually costs.
Adjusts as your codebase, documents, tools, and usage evolve.
Built to deploy across large engineering organizations from day one.
