ContextFusion: The Context Brain Your LLM Apps Are Missing

Author(s): Rohan R Originally published on Towards AI. A deep dive for users who want results and developers who want control TL;DR (For the Impatient) Normal users: Install context-portfolio-optimizer, run cpo compile ./your-docs --budget 4000, and stop overpaying for tokens. Developers: Middleware pipeline that ingests heterogeneous sources → normalizes → precomputes → optimizes via multi-objective knapsack → compiles provider-specific payloads with delta fusion for agents. Both groups get 60–99% token reduction with identical answer quality. Part 1: For Normal Users — “Just Make My LLM Cheaper and Faster” The Problem You Actually Face You’re building with LLMs. Maybe it’s a chatbot over your company docs. Maybe it’s a coding assistant. Maybe it’s an agent that needs to remember context across 20 turns. You keep hitting the same frustrations: “Why is this API call so expensive?” — You’re sending 8,000 tokens when 800 would suffice “Why does it take 10 seconds to respond?” — Latency scales with prompt size “Why does my agent forget everything?” — You’re not managing context deltas across turns “Why do I have to rewrite everything when I switch from GPT-4 to Claude?” — Hardcoded p…

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