Inside SNAP's Source Layer: grounded generation from your own material

Generative AI is only useful in the enterprise when every answer can be traced back to a source you trust. SNAP's source layer is the piece that makes that possible — it's where scattered files become a grounded, queryable knowledge base.

What the source layer actually does

The source layer ingests the material your team already produces — web pages, PDFs, policies, reports, meeting notes — and normalizes it into a structured store. Instead of embedding raw files into a generic vector index, SNAP preserves the origin, section, and role of each passage so downstream reasoning can cite exactly where a claim came from.

That matters because grounded generation is only as trustworthy as the layer beneath it. If the source layer loses context, every answer above it inherits the gap.

Why grounding beats prompt-stuffing

Many teams try to solve enterprise AI by pasting content into prompts or fine-tuning on documents. Both approaches drift. The source layer takes a different path:

• Every passage keeps a pointer back to its origin document and section.
• Access rules travel with the source, so scoped visibility is enforced at retrieval, not after the fact.
• Reasoning agents pull only the passages relevant to the current request, keeping context windows tight.
• Updates to a source propagate — no stale copies floating in a fine-tuned model.

This is the same foundation that supports enterprise memory as a middle layer between raw documents and finished work.

How it fits the rest of SNAP

On top of the source layer sit reasoning, skills, and action. But none of them work without grounded inputs. When a teammate asks for a positioning brief or an HR answer, SNAP walks the source layer first, assembles cited passages, and only then generates. The output arrives with its receipts attached.

For organizations that need models to stay inside their walls, the same layer runs against self-hosted LLMs so source material never leaves the environment.