Built a reusable AI memory infrastructure layer that enables LLM applications to extract, store, update, and retrieve long-term user and conversation knowledge. The system supports semantic and episodic memories, deterministic deduplication and supersession, hybrid lexical + vector retrieval, importance/recency-aware ranking, conversation summaries, and per-user memory isolation. Designed the memory lifecycle from LLM-based extraction through policy-driven validation and persistence, with FAISS-based semantic search and evaluation tooling to measure retrieval quality across memory benchmarks.