Cited Reasoning: How SNAP Answers With Sources Every Time
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A deep dive on SNAP's reasoning layer — how every AI answer is grounded in your own memory and cited back to its source.
The gap between a demo-worthy AI answer and one you'd stake a decision on is citations. SNAP's reasoning layer is built so every response comes with its receipts.
Retrieval before generation
Before the model writes a word, SNAP retrieves the relevant memory chunks scoped to the asker's permissions. The model only reasons over context it's allowed to see, and only over context that actually exists in your memory — not the open web, not its training data.
Every claim, linked to a source
Answers surface with inline citations back to the originating document, thread, or decision. Reviewers can click through, verify, and — if the source is wrong — correct it once, at the source. The correction propagates.
Human-in-the-loop for high-stakes calls
Not every question deserves the same trust threshold. SNAP lets you route sensitive categories — legal, clinical, financial — through a human reviewer before the answer reaches the requester. Low-risk questions flow through instantly.
Why grounded reasoning cuts errors 70–90%
Ungrounded language models hallucinate because they're forced to guess. Grounding in your own memory removes the guesswork for anything your organization already knows. The residual error rate is small enough that a lightweight review gate closes the gap for the questions that matter most.
Agents reason the same way people do
The same reasoning layer serves autonomous agents. An agent handling a customer escalation queries the same memory, respects the same permissions, and returns the same cited answers — which is why agent rollouts on top of SNAP don't require a separate governance model. For more on agent patterns, see the AI Agents Store.