Solve LLM forgetfulness: persistent memory + graph for code context

Many engineering teams using LLMs for code tasks hit a practical limit: models forget context between sessions or can't reliably connect code to historical decisions, which produces incorrect suggestions, duplicated work, and risky blind spots. This problem spans from 50-person startups to 10k+ employee enterprises—roughly the 12M addressable companies—and is most acute for codebase owners, on-call SREs, and security/compliance teams that require provenance and auditability. You could build a persistent-memory layer that couples a vector store for semantic retrieval with a graph store that encodes explicit relationships between commits, PRs, tickets, test results, and architecture diagrams, exposing a single retrieval API and connectors to Git, CI/CD, and issue trackers. Offer hybrid deployment (hosted plus on-prem) with versioning, RBAC, tamper-evident provenance, and low-latency caching so LLMs used in PR reviews, debugging, and incident response get consistent, auditable code context. The timing is favorable: LLM adoption in engineering is accelerating, hosted vector DBs and graph stores have matured, and enterprises are demanding private, auditable memory stacks—supporting a $28.8B annual market calculated as 12M companies × $2.4K ACV. The market score of 88/100 and revenue potential of 82/100 reflect a large, reachable TAM, but expect longer procurement cycles and nontrivial integration costs. To stand out in a medium-competition field, focus on demonstrable provenance, seamless developer workflows, and turnkey hybrid deployments while being honest about the challenges: integrating diverse toolchains, controlling vector and graph storage costs, managing latency, and maintaining retrieval relevance as models and code evolve.