1
Document Ingestion
Documents can be uploaded through a centralized interface.
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Case Study
Enterprise Document Automation
Turning Unstructured Documents into Actionable Business Data
- Document AI
- OCR
- LLMs
- Human-in-the-Loop
- Workflow Automation
Automating the extraction and processing of information from business documents.
01
The Challenge
Many organizations still rely on employees to manually process information contained in documents.
Examples include:
- Invoices
- Forms
- Claims
- Applications
- Purchase orders
- Correspondence
- Scanned PDFs
- Operational documents
Traditional OCR can extract text, but it does not necessarily understand the business meaning of that information.
02
The Product Opportunity
Opportunity
The opportunity was to combine OCR, LLM-based understanding and workflow automation to transform unstructured documents into structured, usable business information.
Core workflow
- 1Document
- 2OCR
- 3AI Extraction
- 4Validation
- 5Human Review
- 6Structured Output
- 7Business System
03
The Solution
2
OCR
OCR extracts text and relevant visual information from scanned documents.
3
AI Understanding
An LLM-based processing layer interprets the extracted information according to the document type and required business fields.
Example invoice fields
- Vendor
- Invoice number
- Invoice date
- Due date
- Line items
- Tax
- Total amount
- Purchase order
4
Confidence Scoring
Rather than assuming every AI extraction is correct, the system assigns confidence levels to extracted information.
This allows uncertain information to be identified for review.
5
Human-in-the-Loop
Low-confidence or exceptional cases can be routed to a human reviewer.
- AI handles high-confidence cases
- Human reviews exceptions
6
Structured Output
Validated information can then be delivered into downstream workflows or business systems.
Potential outputs
- Excel
- CSV
- APIs
- ERP systems
- Internal applications
- Workflow systems
04
AI Architecture
The solution combines
- OCR
- LLMs
- Structured extraction
- Confidence scoring
- Human-in-the-loop
- Workflow automation
- API integrations
Instead of forcing an AI model to make a decision in every situation, the system creates an escalation path for uncertain cases.
Key principle
AI should know when it is uncertain.
05
Product Thinking
A major product challenge was determining the right balance between automation, accuracy and human control.
A fully automated system may create unacceptable errors in sensitive business processes. A completely manual system provides little efficiency improvement.
- Automation
- Accuracy
- Human Control
Key principle
The product therefore uses confidence-based automation to determine when AI can proceed and when human intervention is appropriate.
06
My Role
Product Strategy & AI Workflow Design
Responsibilities
- Business problem definition
- Use-case discovery
- Product requirements
- Document workflows
- AI workflow design
- Confidence-based processing
- Human-in-the-loop design
- User journeys
- MVP definition
- Prototype development
- Integration considerations
- Product roadmap
07
Outcome
The solution provides a reusable framework for automating document-heavy business processes while retaining human oversight for uncertain cases.
The architecture can be adapted to different document types and business workflows.
The most useful AI systems don't simply generate answers. They fit into reliable business workflows.
08
What This Demonstrates
- 1Document
- 2Understand
- 3Validate
- 4Decide
- 5Act
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