Project

PDF Extraction Automation

An AI-first pipeline that converts high-volume business PDFs into validated structured records for downstream workflows.

This project reflects how Mohammed Rafique Kuwari approaches AI automation engineering: define the operational bottleneck, design a reliable data flow, and build outputs that are useful inside real business systems.

Business problem

Operations teams were manually reading invoices, forms, and reports, creating delays and inconsistent data quality.

Approach

Built a multi-stage parser with OCR fallback for scanned pages.
Applied LLM-assisted entity extraction mapped to strict JSON schemas.
Added rule-based validation, confidence thresholds, and human-review queues.
Integrated outputs into internal APIs for real-time downstream processing.

Tech stack

PythonFastAPILLMsOCRPostgreSQLDocker

Architecture highlights

Document intake service with queue-based processing
Hybrid extraction layer (OCR + LLM)
Schema validation and exception handling service
Webhook/API delivery for structured JSON

Expected value

Significantly reduced manual document processing and made PDF to structured JSON output more dependable for finance and operations teams.