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

Enterprises and developer teams struggle to get reliable answers from massive document stores because transformer context windows are limited, cloud LLM calls are expensive, and many regulated customers will not tolerate sending sensitive content off-prem. This pain is acute at large organizations that handle thousands of PDFs and multi-GB data silos—roughly the 2,000,000 enterprises that define a $24.0B addressable market (at ~$12K ACV) where document-AI and developer tooling are mission-critical. You could build a developer-focused platform that automatically chunks and indexes huge files into vector stores, provides adaptive chunking and relevance scoring to control token usage, and routes inference to either local runtimes (llama.cpp/Ollama) or cloud models with transparent cost telemetry and fallbacks. The product would include SDKs and connectors for common repositories, enterprise-grade security controls, and policies to enforce local-only inference where required; the market timing is favorable given the 95/100 market momentum score and an 88/100 revenue potential driven by three converging trends—token-cost optimization, hybrid on-prem/cloud inference demand, and maturing vector/RAG ecosystems. This idea can win by delivering measurable cost-and-latency improvements (conservatively targeting 20–40% lower token spend vs. naive full-doc prompting), turnkey local-model deployment for compliance-conscious buyers, and a developer UX that beats stitching together open-source pieces. Challenges are real: engineering and testing across many model runtimes is complex, on-prem support and enterprise sales cycles are costly, and competition from established RAG libraries and vector DB vendors is medium. If you can execute a secure, well-documented runtime-agnostic SDK and prove economics with a handful of pilot customers, the opportunity is worth pursuing; if not, the integration and sales hurdles will limit ROI.