Persistent AI session-memory using files, startup order, and correction logs (no DB)

AI developers and knowledge workers—roughly 6M potential users—struggle to give autonomous agents and conversational apps durable, low-cost session memory without the complexity and recurring expense of hosted vector databases. High per-query costs, brittle startup ordering, and the absence of clear correction/audit trails degrade agent coherence and slow developer iteration. You could build a file-first persistent-memory platform that combines deterministic startup-order manifests, append-only correction logs, and local or on-device embeddings stored alongside files rather than in a dedicated DB, exposing SDKs for fast semantic retrieval and incremental re-embedding. Make integration trivial for existing agent frameworks and CI so teams can prototype in days, potentially avoiding per-request vector DB costs and reducing infra spend. The timing is attractive: an $18.0B addressable market (6M users × $3K ACV), a market score of 92/100, and converging trends—rapid growth in autonomous agents, better on-device embedding models, and mounting cost sensitivity—create a clear opening for zero/low-cost alternatives to hosted vector stores. To stand out you must deliver production-grade reliability (concurrency control, compact on-disk indices, secure sync) and developer ergonomics (manifests, migration paths, exportable provenance), plus benchmarks that prove comparable recall at acceptable latency. The real challenges are maintaining retrieval quality at scale, handling multi-user conflicts, and convincing teams to trade some performance for simplicity and lower recurring cost, but pilots showing 30–70% infra savings would make the value proposition tangible.