The architectural difference
This isn't RAG. It's something else.
Retrieval-Augmented Generation chunks documents, embeds them, and searches for the closest vector at query time. That works for "answer questions about a PDF." It does not work when the third party at the other end of the table is the IRS, an opposing counsel, or a court. Evidence-grade memory is structured first and similarity-searched second — never the other way around.
Typical RAG stack
- Documents chunked into 512-token slabs
- Source provenance lost at chunking time
- Vector similarity is the only retrieval signal
- Hallucinated answers cannot be traced to a record
- Shared embedding namespace across tenants
- "Memory" is one giant prompt-context window
- No bi-temporal model — last-write-wins
Lossless platform
- Records are atomic, typed, schema-bound
- Provenance signed at ingestion · verifiable forever
- Graph + structure + vector — retrieval is multi-signal
- Every answer cites the exact source record
- Per-tenant: Pinecone namespace, GCS bucket, Postgres RLS, entity graph in the same Postgres
- Memory is a queryable graph, not a context window
- Bi-temporal · amendments don't overwrite history