A document intelligence pipeline demonstration — classification, extraction, validation and confidence-based routing into a review queue.
- Client
- Paperline
- Industry
- Fintech & Banking
- Year
- 2026
- Services
- Document AI & OCRAI Agents & Workflow AutomationSoftware & Product Engineering
Overview
What this project is.
Paperline models high-volume document intake: invoices, forms and contracts arriving as PDFs, scans, phone photographs and email attachments. It demonstrates the pipeline we build to turn that into structured data downstream systems can consume.
Challenge
The problem to solve.
Real documents are messy. Skewed scans, inconsistent layouts, multi-page attachments and handwriting are the normal case rather than the edge case, and an extraction pipeline that only works on clean input solves the easy half of the problem.
Insight
What we noticed.
Extraction is not the hard part — knowing whether to trust the extraction is. Once every field carries a confidence score, the pipeline can split the work: high-confidence documents flow straight through, uncertain ones go to a person. That split is where the time saving actually comes from.
Strategy
The approach taken.
Score everything and route on the score. Classify the document first, extract against the layout appropriate to its type, validate with business rules and cross-checks, then let confidence decide the path. Design the review queue as a first-class interface, because it is where the remaining human time is spent.
Creative Direction
How it looks and moves.
A review interface built for speed: the document on one side, extracted fields on the other, uncertain values flagged and focused in reading order so a reviewer can confirm or correct without reaching for the mouse.
Execution
What the work involved.
Ingestion across PDF, image and email attachment inputs
Deskew, denoise and OCR preprocessing
Document classification before extraction
Layout and table-aware field extraction
Business-rule validation and cross-field checks
Confidence-based routing into a keyboard-first review queue
Deliverables
- Ingestion pipeline
- Classification & extraction service
- Validation rules engine
- Review interface
- System integration
- Throughput monitoring
Stack
- Python
- FastAPI
- OCR engines
- Vision models
- PostgreSQL
- Queue processing
- Docker
What it sets out to achieve
The outcome the work is built for.
This is a capability demonstration, so the outcomes below describe design intent rather than measured performance. We publish numbers only where a client has verified and approved them.
Confidence decides the path
Clean documents pass straight through; uncertain ones reach a reviewer rather than the ledger.
Built for real inputs
Designed against skewed scans and phone photographs rather than idealised samples.
Review designed, not tolerated
The human queue is treated as a product surface, because that is where the remaining time goes.
