Replace Raw Vector Memory With Structured, Versioned AI Memory

A pure vector RAG pipeline returns chunks ranked by cosine similarity. It cannot tell which chunk is authoritative, which is outdated, or which the user explicitly retracted last week. It blurs facts, events, and opinions into a single bag. Production teams patch this with re-rankers, metadata filters, and dedupe logic — eventually reinventing a memory system.

Typed memory, not flat chunks — Background, Fact, Event, Conversation, Reflection, and Skill memory each retrieve differently. Facts dedupe and conflict-check; events stay ordered in time; conversations compress.

Conflict resolution at write time — When new content contradicts stored memory, MemoryLake flags it instead of silently embedding both. You choose the resolution: latest source, confidence weight, or manual.

Versioned memory with rollback — Did last week's ingest poison your retrieval? Roll back the bad commit. Vector stores can't do this.

Pair with your existing vector DB — Keep your document chunks where they are. Use MemoryLake on top for user state, agent state, and structured facts.

1. Connect — Point your ingest pipeline at MemoryLake instead of (or alongside) your vector DB.
2. Structure — MemoryLake classifies each chunk into a memory type, dedupes against prior content, and stores provenance.
3. Reuse — Retrieve at inference. Get back ranked, conflict-free, type-aware memory ready to drop into the prompt.

Teams running production RAG who have outgrown a single vector database — and are tired of writing custom dedupe, re-ranking, and metadata-filtering code to compensate for what vectors don't do.