Many mid-size and large companies struggle to keep organizational context accessible across teams and across AI interactions, which slows support, sales, onboarding, and product decisions. This is an especially acute problem for roughly 300,000 mid+large enterprises where fragmented documents, chat, tickets, and code mean teams constantly re-discover the same information rather than building on shared knowledge. You could build a team-level AI memory platform: a shared, searchable, access-controlled vector store that ingests documents, chats, tickets, code, and metadata, provides incremental summarization, provenance tracking, deduplication, and fast RAG-powered retrieval surfaced via SDKs and prebuilt app connectors — positioned as enterprise knowledge + AI memory at an average $100K ACV toward a $30B addressable market. The timing makes sense: demand for context-aware agents is rising as LLMs are embedded into apps, hosted vector databases and managed RAG services materially lower engineering cost, and distributed workforces increase the value of a single source of team truth; market and revenue potential scores are high (90/100 and 88/100 respectively), though competition is medium. To stand out, prioritize enterprise-grade data governance (fine-grained ACLs, audit trails, encryption), robust retrieval quality (hybrid dense+symbolic search, feedback loops, provenance to reduce hallucinations), and deep app integrations so agents carry context natively — those features create practical defensibility given mature vector infra but require solid engineering. Be honest about challenges: data quality and trust, procurement and compliance cycles, and operational cost of storing and refreshing large indices mean go-to-market should start with high-ROI pilots in support, sales enablement, or onboarding where measurable KPIs can justify $100K+ deals.