Executive Summary

Knowledge workers and teams are increasingly stuck with fragmented LLM chat histories across providers, devices, and apps, making it hard to find prior prompts and answers quickly and causing repeated work and lost institutional knowledge. This is a real productivity drag for professionals—roughly 30M knowledge workers could benefit from faster prompt reuse and discovery. Build a unified search product that ingests and indexes chat histories from multiple LLM providers and apps, using embeddings and vector search with filters for date, model, context, and provenance, plus connectors and surface integrations (web, Slack, VS Code, browser). Include enterprise features like retention policies, audit logs, access controls, and exportable provenance to satisfy governance needs. The TAM is compelling at about $6.0B (30M knowledge workers × $200 ACV), with an 88/100 market score and rising demand as multi-LLM adoption grows and vector search costs fall, and competition is currently medium. Early enterprise spend for auditability and compliance supports the 78/100 revenue potential. You can stand out by combining deep, reliable integrations with strong end-to-end provenance, governance, and prompt-tuned semantic ranking—capabilities many consumer search tools lack. The main challenges are securing integrations, meeting strict privacy/compliance requirements, and building a delightfully fast UX, but the technical feasibility is high given managed embedding services and inexpensive vector DBs.

Market Opportunity

Search across multiple LLM chat histories to find past prompts and answers quickly targets a $6.0B = 30M knowledge workers × $200 ACV total addressable market with medium saturation and a year-over-year growth rate of 15-20% YoY — enterprise AI productivity and knowledge tools adoption (Gartner/IDC 2023-2024 estimates).

Key trends driving demand: Multiple LLM adoption — organizations and individuals routinely use two or more LLM providers, creating fragmented conversational data and demand for central search.; Vector search commoditization — inexpensive vector DBs and managed embedding services make high-quality semantic search feasible for startups.; Enterprise AI governance — companies increasingly require auditability and retention controls for AI interactions, favoring centralized solutions with provenance.; Productivity automation — rising ROI expectations for AI-driven task automation create willingness to pay for tools that reduce repeated effort and speed decision making..

Key competitors include Mem (mem.ai), Rewind, Glean, Deepset (Haystack).

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Search across multiple LLM chat histories to find past prompts and answers quickly

Executive Summary

Market Validation

Market Opportunity

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