Structured field extraction
Extraction of any field from a variably structured document: reference, dates, numerical values, tables, signatures.
LayoutLM · Claude / GPT-4o · Azure Document Intelligence
Domain of expertise
Document intelligence: automatic extraction of industrial documents
Extraction and structuring pipelines for technical documents: drawings, material certificates, compliance files, NC reports, supplier invoices. OCR + LLM, JSON / XML output to ERP, DMS or EDM.
Capabilities
What we build
Automatic classification
Identification of the document type at the pipeline entry: routing to the right extractor based on the document family.
Zero-shot classification · fine-tuning · embeddings
Business-rule validation
Consistency between fields, compliance with a reference (material standards, admissible value ranges), detection of inconsistencies before ERP integration.
JSON Schema rules · custom validators
Batch processing and real-time API
Batch processing on large volumes or via REST API for incoming flows (email, EDM, EDI). Latency 2 to 8 sec / document.
FastAPI · Celery · S3 / Azure Blob
Human review interface
For documents with a low confidence score (< configurable threshold): a review queue with pre-filled fields and highlighting of uncertain zones.
Web interface · confidence score per field
ERP / DMS / EDM integration
JSON / XML output to SAP MM, Oracle, Sage, SharePoint, Alfresco or any IS via API. Error and rejection handling.
REST · SFTP · Webhooks · SAP BAPI
Architecture
Reference pipeline
Each document goes through a processing chain adapted to its structure and its variability. The LLM extracts and structures, the business rules validate.
Pre-processing
Image quality
Deskew, denoise, contrast enhancement, orientation detection. Critical for poor-quality scans.
Layout
Structure analysis
Detection of text blocks, tables, headers. LayoutLM or heuristic rules depending on format variability.
OCR
Text recognition
Tesseract (on-premise), Azure Document Intelligence or AWS Textract. Choice based on data sovereignty constraints.
Extraction
LLM structuring
Prompt engineering or fine-tuning depending on volume. Extraction of target fields with a confidence score per field.
Validation
Business rules
Consistency checks, formatting, value ranges. Routing to human review if confidence is insufficient.
Documents processed
Document types
Any recurring structured or semi-structured document is a candidate. The ROI appears from 500 documents / month on a single type.
EN 10204 material certificates
REACH / RoHS compliance sheets
PPAP files
Dimensioned technical drawings
Acceptance reports
Non-conformity reports
Delivery notes
Supplier invoices
Quality control reports
Customs documents
Results
Observed orders of magnitude
Measured on comparable deployments. They depend on the variability of the formats, the quality of the scans and the business validation rules.
> 92 %
extraction accuracy on structured fields after fine-tuning
2 – 8 s
processing time per document depending on complexity
70 – 90 %
reduction in manual entry time on processed volume
from 500 docs/month
volume from which the ROI becomes observable
Approach
How we proceed
01
Document audit
Catalogue of types, monthly volume, format variability, target IS and validation rules. 1 week.
02
Pipeline benchmark
OCR / LLM test on a representative sample of 100 to 200 documents. Accuracy measurement per type. 2 weeks.
03
Development
Fine-tuning or prompt engineering, business rules, exception handling, review interface. 4 to 6 weeks.
04
Integration
API to ERP / DMS, handling of rejections and duplicates, regression tests. 2 to 3 weeks.
05
Monitoring
Tracking of the average confidence rate, detection of new uncovered formats, updating of the extractors.
Get started
A repetitive document flow to automate?
Tell us about the document type, the monthly volume and the target IS. We estimate feasibility and ROI within 48 hours.