Business Data AI Assistant
Concept for an internal-facing assistant grounded in exports from spreadsheets, manuals, or lightweight systems — citations first, guesses never sold as facts.
Challenge
Teams store know-how across Excel, docs, and inboxes. Search is noisy and onboarding is slow; generic AI chats invent detail if they lack tethered sources.
Approach
Describe an ingestion rhythm from agreed exports (weekly CSV snapshots, handbook PDFs) into a partitioned index where each retrieval returns cited snippets. Add role-aware access stubs and an audit stance: synthetic answers flagged, human reviewer loop for contentious answers — outlined as responsibilities, not software shipped.
Outcome
Clarifies what “grounded” means in scope: corpus boundaries, staleness disclosure, escalation when sources conflict — expressed as workflow design, not live metrics.
Capabilities
- Retrieval pipelines from approved business exports
- Citation-first answering and conflict handling policy (conceptual)
- Ownership model for corpus freshness and corrective edits
Stack
- Document ingestion (conceptual)
- Embeddings + chunking layout
- Vector store + access boundary sketch
Proof points
- Evaluation outline: citation hit vs abstain vs escalate (hypothetical test design).
- Data-handling checklist: PHI/PII stay out unless explicitly scoped (planning artefact).