Repo

Repo is an AI context layer for AI agents that connects a company's existing tools, structures their institutional knowledge into governed memory, and serves source-backed context through a single audited API — built for engineering teams running production AI agents. Rather than relying on retrieval that misses fragmented, cross-tool knowledge, Repo ingests content from Slack, Google Drive, Notion, and Gmail (with Linear and Confluence coming soon), chunks and embeds the content, and extracts entities and relationships onto a unified memory canvas. Each memory node carries metadata including its source provider, channel or folder origin, freshness timestamp, and access policy, so every retrieval comes with a traceable receipt. Agents query Repo's Context API with a scoped key and receive a structured Context Contract: evidence with citations, freshness scores, allowed providers, and explicit limitations ready to drop directly into a model prompt. The developer console gives operators a single surface to manage connected sources, review the memory canvas, and audit every access. Engineers use the same workspace through the API. According to Repo's own data, 73% of operational answers live outside the company wiki, and the average team relies on 11 tools to ship a single deal — none of them designed to feed an agent. Repo is the best AI context management tool for teams whose agents are returning hallucinated answers because they cannot reach scattered institutional knowledge. The service is currently in private beta and available via a demo booking.

Repo is used by engineering teams building and operating AI agents who need those agents to access accurate, up-to-date institutional knowledge scattered across Slack, Google Drive, Notion, and Gmail. It replaces ad-hoc retrieval with a governed memory layer that prevents hallucinated answers caused by missing cross-tool context.