Tzivia compresses any corpus into a self-organizing knowledge graph and hands an AI agent back only what's relevant — in milliseconds, not another full re-read. Built as an MCP server: it plugs into Claude, Cursor, and any MCP-compatible host with no custom integration.
Every number on this page comes from a reproducible evaluation harness, not a demo cherry-picked to look good. Public LongMemEval and LoCoMo results are coming next.
Ingests any corpus — documents, conversations, research — and builds a multi-tier knowledge graph instead of a flat vector dump.
Bridges concepts across the graph through learned, weighted connections — the paths she uses most get faster over time, not just bigger.
Returns the right altitude of detail for each query — a precise fact, or a distilled principle — in milliseconds, with the evidence trail attached.
Raw knowledge chains, tagged with source and trust from the moment they're ingested.
Bridges built across chains — exact and semantic — walkable back to source.
Novel, merged chains clustered from related inferences into higher-order ideas.
Meta-principles spanning domains — the "why," not just the "what."
The core divergence-detection mechanism that powers Tzivia's memory generalizes beyond text. In early research, the same engine is being applied to compare a tumor DNA sequence against a matched healthy one, to map where the two structurally diverge. This is a research application, not a validated clinical tool — we're actively seeking research design partners to test it against public genomic data.
Seeking research partners