Chat with us!We built an intelligent document processing system that ingests invoices, contracts, and forms, extracts structured data with high accuracy, and routes it ...
What used to take a team all week now clears by lunchtime — and the data is cleaner than ever.
Head of Finance Operations
We built an intelligent document processing system that ingests invoices, contracts, and forms, extracts structured data with high accuracy, and routes it into downstream systems — replacing slow, error-prone manual entry.
| Project Detail | Information |
|---|---|
| Project Type | Web |
| Industry | Finance |
| Technologies | Vision-language model, Tesseract / cloud OCR, Python, PostgreSQL, FastAPI, React |
| Delivery Partner | mTouch Labs |
| Primary Outcome | The system processed documents in seconds with high straight-through rates, slashing manual workload and errors. |
Teams keyed data from thousands of varied documents by hand. Throughput was low, error rates were high, and exceptions piled up with no clear triage.
We combined modern OCR with a vision-language model that understands layout and context, validating every extracted field against business rules and routing uncertain cases to a review queue.
The platform classifies documents, extracts and validates fields, and pushes clean data to ERP and accounting systems, with a human review console for exceptions only.
Layout-aware understanding and extraction
Text recognition baseline
Extraction and validation pipeline
Extracted data and audit storage
Processing and review APIs
Exception review console
Catalogued document types, layouts, and target fields.
Built classification, extraction, and validation stages.
Added per-field confidence and business-rule checks.
Built an efficient queue for human exception handling.
Connected downstream systems and a correction feedback loop.
The system processed documents in seconds with high straight-through rates, slashing manual workload and errors.
Intelligent document processing converted a manual bottleneck into a fast, accurate, auditable pipeline that scales with volume.
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View case studyWhat businesses ask us most often about this project and how we built it.
It is the use of OCR plus AI that understands document layout and context to automatically classify documents, extract structured data, validate it, and route it into downstream systems.
On key fields the system exceeds 97% accuracy, with per-field confidence scores and business-rule validation flagging anything uncertain for review.
They are routed to a human review console so staff only touch the small share of exceptions rather than every document.
Yes. The vision-language approach understands varied and unseen layouts far better than template-based legacy OCR.
Reviewer corrections feed back into the system, continuously improving accuracy on your document mix.
mTouch Labs combines AI-powered development with deep industry expertise to deliver solutions faster.
