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Executive Summary

Developers and engineering organizations that rely on LLM agents and copilots today face two correlated pain points: rapidly rising token costs as agents indiscriminately fetch large code contexts, and high false-positive rates that waste developer time and compute. These problems are acute in larger teams and monorepos where repetitive agent queries amplify spending and where simple keyword or vector matches produce misleading results that require manual verification. The product is an LSP-aware proxy that sits between editors/agents and language servers to dramatically reduce tokens and false positives: token-efficient caching, incremental AST- and symbol-level diffs, on-proxy static-analysis filters, and compact semantic summaries to ensure only the minimal, high-signal context is sent to LLMs. It would expose the same LSP surface for drop-in integration with editors, CI, and agent runtimes while offering metrics and hooks to tune trade-offs per-repo or per-agent. This is an attractive moment because roughly 26 million developers represent a $39B developer tools market (assumed $1,500 average annual spend), LSP is broadly supported across editors and CI, and the rapid adoption of agent-driven workflows makes token spend a growing line item on engineering budgets. A conservative benchmark where the proxy cuts token volume by 5x–10x or halves false positives would translate into meaningful cost savings and productivity gains for medium and large engineering orgs. The clearest way to stand out is by delivering measurable reductions (target 5–10x token reduction, ~50% fewer false positives) through standards-based, low-friction integration and by combining LSP signal with lightweight static analysis rather than relying only on vector search. Strengths include near-term integration paths and quantifiable ROI; challenges include the engineering effort to remain compatible with many language servers, handling huge monorepos, and convincing security-conscious customers to route traffic through a proxy—each of which will require hardened performance, privacy controls, and strong benchmarking to win adoption.

Executive Summary

Token-inefficient grep for agents — LSP proxy slashes tokens & false positives

Executive Summary

Token-inefficient grep for agents — LSP proxy slashes tokens & false positives

Executive Summary

Market Validation

Market Opportunity

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