Use cases / Finance and operations
AI Document Processing
Structured data from unstructured documents, with review where it matters.
01
Problem
Teams re-key data from PDFs and emails. Errors are found late, and nobody can show which value came from which page.
02
AI capability
Classify documents, extract fields, validate against rules and systems of record, and route low-confidence items to people.
03
Data
The agent works from the context you connect, not from the open internet by default.
- Inbound documents and emails
- Reference data and schemas
- Systems of record for validation
04
Agent
Document Agent
Extracts and validates fields and routes exceptions to a review queue.
05
Workflow
- 1. Document received
- 2. Classify
- 3. Extract fields
- 4. Validate against rules
- 5. Confidence check
- 6. Review queue or auto-post
- 7. Log source for each value
06
Governance
Governance runs inside the agent at runtime: policy changes what it can actually do.
Can
- Read incoming documents
- Extract and validate fields
- Write to a staging area
- Route exceptions
Cannot
- Post directly to restricted ledgers
- Overwrite validated records
- Process documents above its classification
- Delete originals
Requires approval
- Posting below the confidence threshold
- New document types
- Bulk corrections
Records
Agent identity, Data accessed, Model used, Output, Tools called, Policy applied, Decision, Approval, Action, Outcome.
Suggested starting autonomy: L2 Approve
Start at L2 with review of every batch; raise to L3 for document types with stable accuracy.
07
Outcome
What you measure, so the agent earns more autonomy on evidence:
- Field-level accuracy after review
- Share of documents needing review
- Cycle time per document
- Exceptions found before posting
Questions
How are uncertain extractions handled?
Below the configured confidence threshold they go to a human review queue instead of being posted.
Can we trace a value to its source?
Yes, each extracted value is recorded with its source document and location.