Building an AI Document Processing Pipeline
Document processing remains one of the highest-ROI applications of AI in enterprise. Organizations drown in invoices, contracts, forms, and correspondence that require manual data entry. An intelligent document processing (IDP) pipeline can automate 80-95% of this work. Here is how we build them.
Architecture Overview
A production IDP pipeline has five stages:
- Ingestion: accept documents from email, upload, scanner, or API.
- Pre-processing: normalize orientation, enhance image quality, detect document type.
- Extraction: pull structured data from the document.
- Validation: verify extracted data against business rules and external sources.
- Integration: push validated data to downstream systems (ERP, CRM, database).
Pre-Processing
Raw documents are messy. Our pre-processing pipeline handles:
- Deskewing: correcting tilted scans using Hough transform.
- Denoising: removing scanner artifacts and background patterns.
- Binarization: converting to black and white for cleaner OCR.
- Page segmentation: splitting multi-page documents and identifying page types.
- Language detection: routing to appropriate OCR model based on detected language.
This stage alone can improve downstream extraction accuracy by 10-15%.
OCR and Extraction
Modern extraction goes beyond traditional OCR:
- Layout-aware OCR: models like LayoutLM understand the spatial relationship between text elements — a number next to "Total" means something different than the same number in a line item.
- Table extraction: specialized models for detecting and parsing tabular data, including merged cells and multi-line rows.
- Handwriting recognition: for forms with handwritten fields, models trained on relevant scripts and styles.
- LLM post-processing: after OCR, pass the raw text to an LLM with a structured extraction prompt. The LLM handles ambiguity, context, and formatting better than rule-based parsers.
Validation Layer
Extraction without validation is dangerous. Our validation pipeline includes:
- Format validation: dates are valid dates, numbers parse correctly, required fields are present.
- Cross-reference validation: vendor names match vendor database, PO numbers exist, amounts match expected ranges.
- Confidence scoring: flag fields extracted with low confidence for human review.
- Duplicate detection: identify documents that have already been processed.
Documents failing validation route to a human review queue with the extraction results pre-filled for correction.
Human-in-the-Loop
The human review interface is critical for both quality and continuous improvement:
- Pre-populate forms with extracted data — humans correct rather than re-enter.
- Highlight low-confidence fields to focus reviewer attention.
- Capture corrections as training data for model improvement.
- Track reviewer accuracy and speed to optimize the review process itself.
Over time, as the model improves, fewer documents require human review.
Performance Metrics
For a recent deployment processing logistics documents:
| Metric | Before | After |
|---|---|---|
| Processing time per document | 12 minutes | 15 seconds |
| Data entry error rate | 4.2% | 0.8% |
| Documents processed per day | 200 | 3,000+ |
| Staff needed | 8 FTEs | 2 FTEs (review only) |
Conclusion
AI document processing is mature, proven, and delivers immediate ROI. The key is building a pipeline that gracefully handles the full spectrum of document quality and formats. Start with your highest-volume document type, build the full pipeline including validation and human review, then expand to additional document types.
Related articles
Building Production-Ready RAG Systems
A practical guide to designing Retrieval-Augmented Generation systems that perform reliably at scale — from chunking strategies to evaluation frameworks.
Fine-Tuning LLMs on Enterprise Data
When off-the-shelf models are not enough: a step-by-step guide to fine-tuning large language models on your company data for better accuracy and lower costs.
Vector Databases and Semantic Search in Practice
A hands-on guide to implementing vector databases for semantic search — from choosing the right database to optimizing recall and latency in production.