Lost AI insights — add a searchable semantic memory layer to LLM chats

Knowledge workers increasingly use LLM chat to brainstorm, summarize, and surface insights, but those valuable outputs are transient: threads and prompts become siloed, lexical search fails to surface conversation-derived insights, and reuse across sessions is difficult. This problem affects an estimated 300 million knowledge workers and maps to a roughly $60B addressable market (personal and microteam subscriptions plus enterprise KM spend), so even modest capture rates are economically meaningful. You could build a searchable semantic memory layer that automatically ingests and indexes chat logs, document snippets, and relevant metadata into embeddings-backed vector stores, surfacing provenance-linked snippets, context-aware retrieval during new chats, and privacy-first sync controls; target pricing could be about $200/year for individuals with enterprise licensing for larger organizations. The market is attractive now because LLM usage is mainstreaming, embeddings and production-ready vector DBs have driven down latency and cost, and users increasingly expect AI that remembers and reasons across sessions. To stand out, prioritize high-precision snippet extraction, editable provenance, frictionless UX (chat plugins, IDE-style sidebars, and open APIs) and enterprise-grade privacy and SSO so you can sell to both individuals and teams; the Market Score of 90/100 and Revenue Potential 86/100 reflect real upside if you can execute. Key challenges are integration complexity across platforms, controlling vector-store costs at scale, and earning organizational trust in a medium-competition landscape, but even a small share (1% of the 300M base ≈ 3M users) would imply on the order of $600M ARR at $200/user, making focused experimentation worth considering.