LLM sessions reset to zero — persist user state with a context-store

Many product and dev teams embedding LLMs struggle with sessions that reset to zero and stateless APIs that lose user and application context, forcing engineers to build brittle persistence layers or re-query with expensive token retransmission; given an addressable base of roughly 10 million development/product teams and an $18.0B market (assuming $1,800 ACV), this is a broadly felt operational pain. The problem shows up in chatbots, personalization, agent state, and multi-turn workflows where losing context degrades UX and increases costs. You could build a developer-first context-store: a hosted and optionally on-prem service combining compact embeddings, structured state, and vector retrieval with SDKs and turnkey integrations for LangChain/LlamaIndex and major LLM providers, plus configurable retention, event hooks, and server-side caching to keep latency low and token spend predictable. The product would surface simple primitives for durable session state, automatic retrieval-augmented pipelines, and exportable audit trails so teams avoid reinventing storage, indexing, and retrieval logic. This is an attractive moment because LLM centralization has driven more apps to embed models, embeddings and hosted vector DBs have become both affordable and performant, and SDKs have lowered integration friction—market metrics (Market Score 90/100, Revenue Potential 88/100) reflect that timing. Competition today is low, but success requires honest attention to engineering trade-offs: you must optimize for cost/latency with hybrid hot/cold storage, provide enterprise-grade privacy/compliance (encryption, auditability, on-prem options), and solve cross-provider consistency; if you can execute on those operational challenges and keep the developer experience simple, this product could win practical adoption quickly.