Fix messy GraphRAG knowledge graphs by automated schema modeling and cleanup

Fix messy GraphRAG knowledge graphs by automated schema modeling and cleanup targets a $6.0B = 60,000 mid-to-large enterprises × $100K ACV for AI knowledge-graph and RAG infrastructure annually total addressable market with medium saturation and a year-over-year growth rate of 30% YoY growth for AI infrastructure and developer tooling markets (source: aggregate industry reports and analyst estimates).

Key trends driving demand: RAG adoption — Companies are moving from isolated vector search proofs to production RAG assistants, increasing demand for data modeling and governance.; Rise of hybrid stores — Vector DBs and graph stores are increasingly used together, creating integration and schema alignment needs that tools can solve.; LLM-driven automation — LLMs can infer schemas and generate transformations, enabling automated pipelines but also producing brittle outputs that need validation.; Developer-first buying — Developer and platform teams are demanding self-serve tools that generate deployment-ready artifacts rather than one-off scripts..