Unify fragmented enterprise data via agentic RAG + conversational BI

Mid-to-large enterprises—roughly 120,000 worldwide—regularly struggle with fragmented data across warehouses, lakes, third-party SaaS and internal docs, producing slow, untrustworthy BI and blocking cross-system automation. Analytics, IT and product teams end up spending on average the equivalent of a $200K ACV per account on BI and AI engineering without getting consistent, end-to-end natural-language answers that are grounded in source evidence. You could build an agentic RAG platform that pairs scalable embedding pipelines and hosted vector stores with multi-step LLM agents and a conversational BI front end to deliver grounded, actionable answers and automated workflows with provenance and lineage. The market is attractive now because vector infrastructure maturation, rising RAG adoption and the emergence of agentic automation materially reduce latency, cost and integration barriers; the addressable market is about $24.0B (120,000 accounts × $200K ACV), and internal scoring suggests high market interest (market score 92/100, revenue potential 84/100). To stand out, focus on pragmatic differentiation: ship hardened connectors for the top ~20 enterprise systems, surface verifiable provenance and role-based governance by default, and provide a developer-first SDK to compose agents into audit-ready business workflows. The strengths are clear—measurable automation ROI and trustworthy outputs enabled by modern infra—while the real challenges are long procurement cycles, integration complexity, and operationalizing agent safety, latency SLAs and auditability for regulated environments.