Document QA cost and freshness pain - RAG-first vs fine-tune decision tooling

Document QA cost and freshness pain - RAG-first vs fine-tune decision tooling targets a $12.0B = 120,000 knowledge-heavy enterprises x $100K ACV. Assumes knowledge-work verticals worldwide (legal, finance, support, healthcare, product) buy enterprise QA and knowledge automation suites at six-figure annual contracts. total addressable market with medium saturation and a year-over-year growth rate of 30-40% growth in enterprise AI knowledge tooling adoption, driven by LLM integrations and vector DB adoption.

Key trends driving demand: Vector DB maturity -- managed vector databases reduce infra burden so RAG becomes practical for more teams, lowering time-to-value; Open-model and cheaper inference -- cheaper LLM costs make RAG pipelines economical versus repeated fine-tune cycles; Compliance and auditability -- enterprises demand source-backed answers and provenance, favoring retrieval over opaque fine-tuned weights; Tooling standardization -- frameworks like LangChain and LlamaIndex create repeatable patterns, enabling productized decision tooling.