Solution
Intelligent Document Processing
Turn invoices, forms, contracts and correspondence into accurate, structured data automatically, with people reviewing only the cases that genuinely need attention.
The problem
Documents still drive a surprising amount of work
Despite digital transformation, many processes begin with a document: a supplier invoice, an application form, a delivery note, a contract. Someone reads it, types key details into a system and files it away.
Manual handling is slow, inconsistent and difficult to scale, and errors at this first step ripple through everything that follows.
Causes and impact
Why it happens and what it costs
Varied layouts
Documents arrive in many formats from many senders.
Limited legacy OCR
Template-based tools break when layouts change.
No validation step
Extracted data is not checked against business records.
Operational impact
- Processing backlogs at busy periods
- Data entry errors and rework
- Delayed payments or decisions
- Little visibility of document volumes and status
The solution
Read, understand, validate, route
Our document processing pipeline classifies incoming documents, extracts relevant fields using modern document AI, validates results against business rules and reference data, and routes low-confidence items to a review screen.
Approved data flows into your systems, and the original document remains linked for audit.
Main features
Key features
Document classification
Identify document types automatically from mixed inputs.
Field extraction
Capture header fields, line items, tables and dates.
Validation rules
Cross-check totals, references and master data.
Review interface
Side-by-side document and data with confidence highlights.
System integration
Post approved data to ERP, CRM or case systems.
Secure storage
Controlled access and retention for original documents.
High-level workflow
How it works
Illustrative example: accounts payable
Supplier invoices arriving by email are classified and their header and line-item data extracted. Totals are checked against purchase orders, matching invoices are queued for payment approval and mismatches are flagged for the finance team.
- 1
Ingest
Documents arrive via email, upload, scanner or API.
- 2
Classify
Type identified and appropriate model selected.
- 3
Extract
Fields captured with confidence scores.
- 4
Validate
Rules and reference data checks applied.
- 5
Review and post
Exceptions reviewed; approved data posted to systems.
Integration requirements
Systems typically involved
- Email inboxes and upload portals
- ERP and accounting systems
- Document management systems
- CRM and case management
Implementation considerations
Document variety
Gather representative samples of every major document type.
Confidence thresholds
Decide which fields require review below which confidence.
Personal data
Apply masking and retention rules to sensitive documents.
Feedback loop
Use reviewer corrections to improve extraction over time.
Potential benefits
What this solution is designed to improve
Actual results depend on your starting point, data and adoption; we agree how to measure them during discovery.
Less manual entry
Staff review exceptions instead of typing every field.
Higher data quality
Validation catches errors before they spread.
Scalable throughput
Handle peaks without temporary staff.
Clear audit trail
Every value linked to its source document.
Explore more
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Learn more about Fintech & BankingFAQs
Frequently asked questions
Answers to the questions we hear most often. If yours is not covered here, our team is happy to talk it through.
Modern document AI can read some handwriting, but accuracy varies. We test on your samples and design review steps accordingly.
They are highlighted in a review screen where a person confirms or corrects them before data is posted.
Drowning in documents?
Send us sample document types and volumes and we will assess automation potential.
