Grounded answers with citations: how SNAP keeps high-stakes AI traceable
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When AI answers drive real decisions, every claim needs a source. Here's how SNAP grounds outputs in your own material and cites them back.
Hallucinations are a governance problem, not a UX problem. When AI answers feed hiring, compliance, or roadmap decisions, every claim needs a traceable source. Here's how SNAP grounds outputs in your own material.
Grounding starts before generation
Before the model drafts anything, SNAP resolves the query against your enterprise memory — pulling the specific nodes, passages, and captures that are actually relevant. The model doesn't get free rein over the whole graph; it gets a scoped, cited context window built from your work.
The deeper mechanics are covered in the grounded-sources layer of a CAIO agent.
Every claim carries its receipt
Each sentence in a SNAP answer points back to the source node it came from — a specific PDF page, a voice note timestamp, a captured screenshot. Reviewers can open the source in one click, which turns AI review from "trust me" into "check my work."
Role scoping and confidence, not just retrieval
Grounding also means respecting who's allowed to see what. Answers are scoped by role, and low-confidence responses are flagged rather than smoothed over. For governance context, see the Responsible AI-by-Design Framework.