Reasoning Search Explained: How SNAP Grounds Every Answer in Your Own Sources

Generic chatbots answer confidently and cite nothing. Reasoning Search flips that default: every answer SNAP produces is grounded in your own captured sources, with a source chip you can click to jump back to the exact page or PDF passage.

Why grounding matters

Hallucination isn't a UX problem — it's a trust problem. For regulated teams, an unsourced answer is worse than no answer because it forces a human to redo the work to verify it. Reasoning Search removes that tax by making the citation the primary artifact, not an afterthought.

How Reasoning Search works under the hood

• Scoped retrieval — the search only considers snaps inside the agent's assigned scope, so answers can't leak from another user's memory.
• Cited synthesis — the model composes an answer only from retrieved passages and attaches source chips inline.
• Traceable jumps — clicking a chip opens the original snap at the referenced location, whether that's a web page, a PDF page, or a voice note transcript.

What changes when citations are automatic

Review cycles shrink. Instead of debating whether the model "knows" something, the reviewer clicks the chip and reads the source. That single behavior change is what makes SNAP usable inside workflows that have compliance oversight — the same pattern we describe in Real-Time AI Compliance Checks.

Where it fits in a responsible AI stack

Grounded answers are a necessary condition for responsible deployment, not the whole story. Governance, access control, and audit trails matter too — see the Responsible AI-by-Design Framework for the wider picture. Reasoning Search is the piece that makes the audit trail possible in the first place.