We built a document AI pipeline that turns unstructured PDFs and scans into clean, validated data ready for ERP and back-office systems. The engine handles invoices, IDs, forms, and contracts with mixed layouts and noisy scans.
The flow combines classical OCR with layout-aware parsing and LLM-based field extraction. Fields are validated, scored for confidence, and routed through a human-in-the-loop review when needed, keeping both throughput and accuracy high.
Operators review only low-confidence or flagged documents, while the majority pass straight through to ERP with a full audit trail of what was extracted, when, and by which model.
Manual document processing was:
We introduced a flexible document AI pipeline:
The Document AI Extractor delivered:
The extractor runs as a pipeline that ingests documents from uploads, S3 buckets, or email inboxes, processes them with OCR + LLM parsing, and pushes structured records into ERP/line-of-business systems via webhooks or APIs.
Works across vendors and document variants without hard-coded coordinates, reducing configuration overhead as formats change.
Supports different field sets per document type (invoices, IDs, POs, delivery notes) with reusable parsing logic.
Reviewers can correct fields, accept suggestions, and add comments, feeding back into model and prompt tuning over time.
Every document keeps raw OCR text, extracted values, confidence scores, and user edits for downstream audits and investigations.
The pipeline is built to be extended with new document types and downstream systems, while keeping the core ingestion, extraction, and validation stages consistent.
