Summarize huge files by chunking + local-or-cloud LLM inference

Summarize huge files by chunking + local-or-cloud LLM inference targets a $24.0B = 2,000,000 enterprises x $12K ACV (enterprise document-AI & developer tooling market) total addressable market with medium saturation and a year-over-year growth rate of 28% estimated growth for AI-assisted developer/document tooling.

Key trends driving demand: LLM-context-and-cost-optimization -- rising demand for chunking/RAG to handle long docs while controlling token costs; Hybrid-on-prem-cloud-inference -- enterprises demand local runtimes (Ollama/llama.cpp) for privacy/compliance; Vector-databases-and-RAG-standardization -- easier integrations and mature SDKs speed adoption; Developer-first-AI-tooling -- dev teams prefer SDKs/CLI that integrate with CI/CD and pipelines.