This searches a distributed ring of cortex nodes — each holding a shard of millions of research papers, Wikipedia, books, and open-science papers — with Allivista's verbatim recall. A query hits the first cortex; if the exact phrase isn't in its shard, it forwards to the next cortex in the ring, and the next, until the passage is found or every cortex has been searched. Each node jumps straight to the match by checking only a phrase's rarest word — no scanning, no LLM, no embeddings. Change one word and it returns nothing: recall is exact.
Most "AI memory" is embeddings plus vector search: it returns similar chunks and can miss the exact fact or hallucinate around it. Allivista stores text in a structural index and returns the exact passage. When the corpus is too big for one machine, it's sharded across cortex nodes — each encoded independently (in parallel, on its own cores) — and a query rings around them until it hits. Add a cortex, the mesh gets bigger; nothing else changes.
There's no language model in the loop here — it's a pure structural lookup, exact and near-free to serve. The real tool does the same over your notes and an agent's memory, end-to-end encrypted, and there's a companion cortex that reasons over 4.3M papers for discovery. Under the hood it's GeoLang, which encodes meaning as structure.
Add Allivista to Claude, Cursor, or any MCP client in a couple of minutes — verbatim recall and encrypted memory for your own work, plus a structural fact-check over millions of papers.