Hey HN,

  I built GrantAi because RAG kept failing me. Vector similarity returns content
  that "looks like" your query — but similar isn't correct. Ask for "HIPAA
  penalties" and you might get GDPR fines because the embeddings are close.

  GrantAi is deterministic. Every piece of knowledge gets a unique ID at
  ingestion. It is recall, not a search. You get the exact content you
  stored — verbatim, with attribution, in milliseconds.

  The result: 97% reduction in tokens sent to the LLM.

  Key differences from Mem0/Zep/Letta:
  - No vectors, no embeddings, no similarity search
  - Exact recall, not approximate
  - Full audit trail with source attribution
  - 100% local, AES-256 encrypted, zero data egress

  Works with Claude Code, Cursor, any MCP client. Multi-agent memory sharing built
   in.

  GitHub: https://github.com/solonai-com/grantai

I am happy to answer your questions.