Case study · AI Systems
AI Document Workflow
An AI-powered workflow that extracts structured information from unstructured business documents.
- Discipline
- AI Systems
- Scope
- Intake, AI extraction, validation, export
- Stack
- Python, Anthropic, OpenAI, PostgreSQL, Docker
The problem
The problem
Business documents arrived as PDFs, scans and email attachments in many formats. Key fields were typed into internal systems manually, one document at a time.
The approach
How we built it.
- 01Collected representative documents and defined the target fields.
- 02Built an extraction workflow combining parsing, OCR and language models.
- 03Added validation rules and a review queue for low-confidence results.
- 04Delivered structured output directly into the existing internal system.
System components
- Intake
- Parsing & OCR
- LLM extraction
- Validation
- Review queue
- System export
The outcome
Automated document processing.
- Document processing runs automatically.
- Uncertain results go to a person, not straight into the system.
- Structured data is available as soon as a document arrives.
Have a processthat shouldrun itself?
Tell us what is slowing you down. We’ll figure out what can be automated, engineered or rebuilt.