DejaQ is a middleware architecture designed to optimize LLM costs and accuracy for organizations. It builds a shared "organizational memory" by tailoring responses to business teams with similar identities and interests. Through user feedback, high-quality answers are stored as "Golden Records" in ChromaDB. Its core logic uses Semantic Caching and a Universal Normalizer to provide immediate local responses for recurring queries, preventing redundant external API calls. Cost savings are maximized through hybrid routing: simple prompts are handled locally by models like Llama, while only complex reasoning is escalated to expensive models like Gemini or GPT. This ensures high precision based on peer-validated data and a drastic reduction in financial overhead.