Extract useful information from documents automatically. AI that reads invoices, contracts, forms, and reports so your team does not have to.
Businesses run on documents. Invoices, contracts, applications, reports, and forms contain critical information, but extracting it requires manual effort. Document intelligence uses AI to read, understand, and extract structured data from documents automatically.
Document processing creates bottlenecks:
Manual data entry: Staff spend hours typing information from documents into systems.
Inconsistent formats: Every supplier, customer, or partner sends documents differently.
Error rates: Manual processing introduces mistakes that cause problems downstream.
Slow turnaround: Documents queue waiting for human attention.
Scaling limits: Processing more documents means hiring more people.
AI addresses these constraints.
Modern AI can extract meaning from documents:
Read text: Process typed, printed, and handwritten content accurately.
Understand structure: Recognise tables, forms, sections, and layouts.
Extract data: Pull out specific fields like amounts, dates, names, and references.
Classify documents: Identify document types and route appropriately.
Validate content: Check extracted data against business rules and external sources.
Integrate: Push extracted information into business systems.
AI handles a wide range of business documents:
Financial documents: Invoices, receipts, purchase orders, statements, expense claims.
Contracts and legal: Agreements, terms, amendments, correspondence.
Applications and forms: Customer applications, employee forms, registration documents.
Identity documents: Passports, driving licences, utility bills for verification.
Operational documents: Delivery notes, inspection reports, certificates, manifests.
Reports and correspondence: Internal reports, emails, letters, memos.
Document intelligence delivers measurable improvement:
Processing speed: Documents handled in seconds rather than minutes.
Accuracy improvement: Reduction in data entry errors.
Cost reduction: Less manual effort per document processed.
Faster turnaround: Documents do not queue waiting for attention.
Scalability: Volume increases without proportional staffing.
Staff redeployment: People focus on exceptions and decisions rather than data entry.
Document intelligence combines several AI capabilities:
Optical character recognition: Converting images and scans to machine-readable text.
Layout analysis: Understanding document structure and relationships.
Entity extraction: Identifying and pulling out specific information types.
Classification: Determining document type and routing.
Validation: Checking extracted data against rules and reference sources.
We select and combine technologies appropriate to your document types.
Document intelligence projects follow a structured path:
Document analysis: Understanding your document types, volumes, and variation.
Field mapping: Defining what information needs extraction.
Model development: Training or configuring AI for your specific documents.
Integration: Connecting to your business systems and workflows.
Validation: Verifying accuracy before relying on automated extraction.
Deployment: Launching with appropriate monitoring and exception handling.
Improvement: Refining based on real processing results.
Not every document processes cleanly. Effective systems include:
Confidence scoring: Identifying extractions that need human review.
Review interface: Efficient tools for checking and correcting uncertain results.
Learning loop: Using corrections to improve future accuracy.
Exception routing: Escalating documents that AI cannot process.
The goal is handling volume automatically while ensuring accuracy.
Document automation works best when you are explicit about quality and exceptions.
Define “good enough” accuracy per field. Some fields can tolerate minor errors; others cannot. We set thresholds and escalation rules accordingly.
Plan for variability. Suppliers and customers change templates. A resilient pipeline needs monitoring and a process to update models and rules.
Design the exception workflow. The human review interface and routing often determines whether automation actually saves time.
Use these prompts to pressure-test scope and success criteria.
“Which document types should we automate first, and which fields drive the most manual effort today?”
“What validation rules and systems of record should we use to check extracted data?”
“What exception rate is acceptable, and what is the fastest human review workflow when confidence is low?”
Not always. Many workflows start with pre-trained extraction plus configuration, then add custom models for the hardest templates.
We evaluate on representative inputs, use the right OCR and layout tools, and route low-confidence cases to human review.
Validation rules, confidence thresholds, audit logs, and a human-in-the-loop review process for uncertain extractions.
Yes. We connect to finance, ERP, CRM, and workflow systems so extracted data becomes operational, not just a spreadsheet.
Time saved per document, error reduction, queue time reduction, and the stability of performance as templates change.
