Company Brain - Unified Data Query Layer

Engineers waste time rebuilding context retrieval and memory management for AI agents across projects. A centralized platform ingests data from all company apps (Slack, Notion, GitHub, etc.) into a queryable vector database that agents can access without rebuilding integrations. Target users: engineering teams and AI builders.

The explosion of internal AI agent projects inside mid-size companies has created a real redundancy problem — every team rebuilds the same Slack-to-vector-DB pipeline independently, and that waste is now visible enough that engineering leads are actively looking for a solution. Glean is the closest incumbent, though it targets search for end users rather than serving as a programmatic memory layer for agents, which leaves genuine whitespace on the API-first, developer-facing side. The $2k–10k/mo revenue band is plausible for early design partners but undersells the ceiling if the product earns a true infrastructure role — the risk is it gets stuck in that band because teams treat it as a convenience tool they can replace rather than a dependency they won't. The single most likely failure mode is that the major platforms (Notion, Slack, GitHub) keep improving their own API surfaces and native AI integrations until the integration-aggregation moat disappears before the product can lock in switching costs.