Example: AI Search Across Project Documents (Grounded, Auditable)

Scenario

A PM asks:

“What does the spec require for fire-rated drywall on level two?”

Inputs

  • User query (chat, voice, or mobile)
  • Project document corpus
  1. Step 1: Document Ingestion

    Documents are parsed, structured, and indexed.

  2. Step 2: Query Interpretation

    The system determines relevant document types and sections.

  3. Step 3: Retrieval (Grounded Search)

    Relevant document fragments are retrieved from the project vector index.

  4. Step 4: Answer Construction

    The answer is generated only from retrieved sources.

  5. Step 5: System Use of Results

    Results can trigger RFIs, checks, or follow-on workflows.

Outputs

  • Grounded answer
  • Source references
  • Optional execution actions

Failure Modes

  • Document missing → explicitly stated
  • Conflicts → surfaced, not hidden

Key Distinction

AI retrieves and grounds before answering.

Why This Matters

This is not “chat with docs.” This is document-grounded execution intelligence.

Summary

Search produces defensible answers tied to execution.