A production-ready platform for automating the extraction of structured information from complex business documents. The system combines machine-learning models, rule-based validation, and configurable processing pipelines to process large volumes of semi-structured PDF data.
Problem
Rather than focusing on a single document type, the platform was designed as a reusable framework for document processing. It provides a consistent architecture for extraction, validation, storage, and review workflows while supporting multiple document formats and use cases.
Approach
The project includes automated testing, containerized deployment, database integration, monitoring, and continuous delivery pipelines to ensure reliability in production environments. By combining machine learning with targeted validation rules, the platform improves extraction quality while reducing manual effort and operational overhead.
My role and contributions
- Designing and implementing PDF document extraction pipelines
- Building OCR-aware validation and quality-control mechanisms
- Developing reusable processing components
- Maintaining MongoDB-based data storage and retrieval
- Containerization and deployment with Docker
- CI/CD automation using Jenkins
- Testing, monitoring, and production support
Architecture
The public version of the architecture centers on ingestion, text extraction or OCR, normalization, structured extraction, validation, storage, and review. Machine-learning components handle document understanding where useful, while deterministic checks protect fields that need stronger guarantees.
Related pages
- PDF Document Processing in Python: OCR, Extraction, Validation and RAG
- How to Extract PDF Sections Reliably When Headings Are Missing
- LlamaIndex PDF Querying
- RAG and document processing systems
- Technical skills
Explore related work
For broader context, see Jenifer's research overview, technical skills, and contact page.