Context
Antonii worked as an AI Integration Engineer on IDP architecture for processing long, unstructured documents. The case is private/client work, so implementation details are intentionally summarized.
Problem
Manual form filling from 20-50 page documents is slow and error-prone. The system needed to extract structured data while handling layout variation and imperfect OCR.
Constraints
- Long unstructured documents.
- OCR quality and page layout variability.
- Async backend requirements with queues, caching, and storage.
- Production-MVP expectations around reliability and reviewability.
Approach
The architecture combined OCR, PyMuPDF processing, Smart Anchor extraction, LLM orchestration, Pydantic validation, FastAPI, queues, caching, Docker, and AWS S3 storage.
Implementation highlights
- Smart Anchor algorithm for stable extraction from variable document layouts.
- LLM orchestration around OCR and PDF parsing outputs.
- Async FastAPI backend with queues and caching.
- Pydantic models for structured validation.
Result
Reported outcomes included estimated form-filling time reduced from 40 minutes to about 3 minutes and more than 95% extraction accuracy for 20-50 page documents.
What it demonstrates
Document AI architecture, async backend engineering, LLM orchestration, OCR pipeline design, and careful handling of private client work.