Controlled document classification and extraction
A document-heavy B2B intake process needed structured handling for incoming files before information could be reviewed or routed.
Problem
Document intake required classification, PDF handling, extraction, validation and routing. An isolated AI demonstration would not be enough for operational use.
Existing systems
- Document intake
- PDF processing service
- Document analysis and extraction services
- Workflow and API test harnesses
Constraints
- Extraction confidence had to lead to validation or human review rather than an assumed autonomous decision.
- PDF handling and page-level processing needed explicit validation.
- Document contents, test payloads and endpoint details were kept outside the public case study.
Solution architecture
Intake moves through classification, OCR or AI extraction, validation and routing, with human review points where confidence or document quality is insufficient.
Text alternative
- Document intake
- Classification
- OCR / AI extraction
- Business validation
- Approval / exceptions
- ERP / DMS / SharePoint / SQL
- Reporting / monitoring
Implementation
The work established a controlled document-processing pattern using analysis and classifier artifacts, PDF processing, validation and API test harnesses. The pattern is designed to connect later to workflow and business systems rather than stand alone as a chatbot.
Technologies used
- Azure Functions
- Python
- PDF processing
- Document analysis APIs
- API test harnesses
Outcome
- Established a controlled document-processing pattern.
- Created clear validation and human-review points where confidence is insufficient.
- Made the next workflow and system-integration decisions easier to scope.
Lessons / reusable pattern
- Document AI is most useful when classification, extraction, validation and routing are designed as one process.
- Human review is a deliberate control for uncertain documents, not an exception to be hidden.