AQEA Compress is an AI infrastructure project focused on extreme semantic embedding compression for large-scale retrieval, RAG, and agentic systems. The system transforms dense embeddings into ultra-compact representations (up to 585× compression) while preserving global structure and semantic neighborhoods — without retraining models or requiring GPUs. AQEA Compress operates as an intelligent optimizer over embedding space, enabling higher vector density, reduced memory bandwidth, and scalable semantic memory for multimodal workloads (text, image, hybrid). Key properties: – CPU-only processing – No transformer fine-tuning or retraining – Dynamic modes for precision retrieval vs exploratory discovery – Compatible with existing vector databases and hybrid search pipelines The project addresses a core bottleneck of modern AI systems: semantic memory scalability, where storage and bandwidth constraints dominate long before model inference. AQEA Compress is designed as a foundational layer for next-generation AI infrastructure, not an application-level optimization.