The Challenge
A mid-sized manufacturing company faced a growing data problem: over 10,000 unstructured PDF datasheets and supplier contracts had to be manually transferred into their ERP system every month. Each datasheet contained technical specifications, material compositions, and certification information in varying formats.
Manual data entry was not just time-consuming — it was error-prone. Inconsistent formatting, low-quality scans, and varying document structures resulted in an error rate exceeding 12%. Incorrect material data in the ERP system caused production delays and faulty orders.
The existing team could no longer handle the growing document volume without hiring additional staff.
Our Approach
Blueprint Phase: Data Audit and Feasibility Analysis
We analyzed a representative sample of 500 PDFs and identified 23 recurring document types, each with distinct extraction rules. The feasibility analysis showed that 87% of documents could be fully automated — the remaining 13% required human review for edge cases.
Brain Phase: Pipeline Design
Based on the audit, we designed a multi-stage processing pipeline: PDF ingestion, OCR recognition, rule-based extraction, validation against business rules, and ERP API integration. Each stage was designed as an independent microservice.
Hands Phase: Implementation
The pipeline was developed iteratively — document type by document type. Each new type went through a cycle of test extraction, rule refinement, and validation against historical data.
Architecture
PDF Ingestion and Preprocessing
Incoming PDFs are automatically classified and placed in a processing queue. Image-based PDFs undergo preprocessing (deskewing, contrast optimization) before OCR recognition.
Rule-Based Extraction
For each of the 23 document types, a specific extraction ruleset exists. The engine recognizes tables, key-value pairs, and structured sections, mapping them to the ERP data model.
Validation and Quality Assurance
Extracted data is validated against business rules: material codes must exist, quantities must be plausible, certifications must have valid references. Documents with low confidence are flagged for manual review.
ERP Integration
Validated data is written directly to the ERP system via a REST API. A monitoring dashboard shows processing status, error rates, and throughput in real time.
Results
- 90% less manual data entry — automated extraction replaces months of manual work
- 10,000+ PDFs per month — scalable batch processing without additional staff
- Error rate below 2% — validation rules reliably catch edge cases
- ROI in 3 months — investment recovered through saved personnel costs
- Audit trail — every extraction is traceably documented
Facing a Similar Challenge?
Unstructured documents slowing down your processes? We analyze your data flows and develop an automated solution. Talk to us or learn more about our AI automation services.
