Prevent cross-project AI context bleed via per-project context isolation and orchestration

Many engineering and AI teams in SMBs and dev organizations (addressable ~12M organizations) face cross-project AI context bleed: agents, chatbots, and RAG pipelines can surface irrelevant or proprietary context from other projects, producing incorrect outputs, IP leakage, and audit gaps. This problem scales as teams spin up more agents and projects, creating real operational, legal, and productivity risks that are measurable in missed SLAs and costly incident reviews. You could build a per-project context isolation and orchestration platform that provisions per-project vector stores, enforces fine-grained access controls and cryptographic provenance, routes queries through an orchestration layer with LLM connectors and caching, and exposes lightweight SDKs and CI/CD hooks for seamless adoption. Targeting a $3K ACV benchmark yields a $36.0B addressable market (12M x $3K), and timing is favorable: embedding/vector-search maturity, proliferation of multi-agent workflows, and rising enterprise governance demands combine to create strong buyer intent (market score 92/100; revenue potential 84/100). To stand out you must prove isolation end-to-end (signed provenance), deliver a low-latency hybrid vector architecture, and ship enterprise-grade IAM/compliance and integrations to compete against medium-strength competition from cloud providers and open-source projects. The main challenges are broad toolchain integration, controlling storage/compute costs for isolated indices, and winning adoption against incumbent workflows, but measurable reductions in leakage risk and clear auditability can create defensible ARR and strong retention if executed well.