Skip to content
Beta

Memory

Grounded retrieval over your own material, with receipts.

What it does

Documents, transcripts and uploads are chunked, embedded and indexed, then retrieved by a hybrid of vector similarity, keyword match and graph centrality at answer time. Retrieved passages carry receipts, so an answer can point at the source that produced it, and access rules filter the corpus before retrieval rather than after.

Why it matters

An agent that cannot cite is an agent you cannot check. Retrieval with receipts turns "the AI said so" into "this document says so, here it is", which is the difference between a demo and something you would let talk to a customer.

How it works

  • Access filtering happens before retrieval

    The corpus an agent can search is already narrowed by policy, so a permission mistake cannot surface as a leaked citation.

  • Hybrid scoring, not vector-only

    Vector similarity is combined with keyword match and graph centrality, which is what keeps exact identifiers and rare terms findable.

  • Answers carry grounding receipts

    The passage that produced a claim is attached to the claim, rather than reconstructed afterwards.

Questions

Does the agent remember our conversations?
Not in the way "memory" usually implies. This is retrieval over material you supply. Conversation history is kept per session; it is not distilled into a persistent profile.
Why is this beta rather than live?
The retrieval pipeline works. The vector grounding path is code-complete but its deployment and backfill are not finished, so we will not call it live.
Back to the story

That was step 5 of 6: the agent consults its bag. Next, tasks, delegation edges, decisions and artifacts persist as rows you can replay, and the spend is already accounted for.