Semantic Code Search for AI Agents

A token-efficient code search tool (like grep but smarter) that helps AI coding agents find relevant code in large codebases without reading entire files or spinning up subagents, saving 98% of tokens compared to traditional grep.

AI coding agents hitting context limits in large codebases is a real, documented pain point that's emerged directly from the explosion of agentic coding tools like Cursor, Copilot Workspace, and Claude Code in 2024-2025. Greptile is the closest incumbent but targets human developers rather than machine clients, leaving the agent-native, token-optimized niche genuinely open. The $2k-10k/mo revenue band is plausible if priced per API call or token saved, since the value proposition is quantifiable and the buyer (a dev team running expensive agent workflows) has direct cost visibility. The biggest risk is that foundation model providers or agentic framework layers like LangChain or OpenAI's tooling bake in smarter retrieval natively, commoditizing the problem before any standalone tool reaches meaningful retention.