Executive Summary

Knowledge workers across product, sales, engineering and legal spend significant time repeatedly feeding the same context into multiple AI assistants; we estimate a 300M global knowledge-worker base that spends roughly $400/year on productivity tools, implying a $120B addressable market for reducing that waste. The problem is acute for teams using multiple specialized AIs—writing, search, coding—where lost context causes duplicated effort, inconsistent outputs and time costs measured in hours per week per person. The product would be a persistent personal and team memory layer: a permissioned, auditable vector store with per-object metadata, fine-grained access controls, SDKs and connectors that inject vetted context into any AI or tool on demand. Key features would include encrypted storage, audit logs for compliance, delta/versioned memories, cross-agent connectors, and low-latency retrieval tuned to both chat and code-assistant workflows. The timing is favorable because embeddings and vector search are now commoditized, making memory retrieval inexpensive and reliable, and because enterprises are actively demanding auditable, permissioned context stores rather than ad-hoc prompt stuffing; combined with the market signals (market score 95/100 and revenue potential 94/100) this implies large, near-term enterprise willingness to pay. That said, the main challenges are integration complexity across many AI services, preventing leakage of sensitive data into third-party models, and driving behavior change so users trust and adopt a single memory layer. To differentiate, focus on enterprise-grade governance and interoperability: strict RBAC, consented sharing, certified connectors, and an open ingestion API so memories can be used by any assistant without embedding secrets in prompts. Building strong developer tooling and partner integrations (SSO, SIEM, SOC 2/ISO certifications) will raise switching costs and address the principal challenges of security and adoption, though success will still require careful product UX and a roadmap for standardizing cross-AI memory schemas.

Market Opportunity

Stop Repeating Yourself to Every AI — Persistent Personal Memory targets a $120.0B = 300M global knowledge workers x $400/yr average spend on productivity & knowledge tools total addressable market with medium saturation and a year-over-year growth rate of 35% (AI-assisted productivity/KM adoption growth).

Key trends driving demand: Multi-AI usage -- teams are using multiple specialized assistants (writing, search, coding), creating friction around repeated context-sharing.; Vectorization & retrieval augmentation -- embeddings and vector search have commoditized memory retrieval for LLMs, enabling efficient persistent state.; Privacy & data governance -- enterprises require auditable, permissioned context stores rather than embedding secrets in prompts or third-party LLM calls..

Key competitors include Mem (mem.ai), Tana, Rewind, Pinecone (adjacent — vector DB), Notion AI (adjacent workaround).

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Stop Repeating Yourself to Every AI — Persistent Personal Memory

Executive Summary

Market Validation

Market Opportunity

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