AI-Powered Document Processing Platform
Replaced manual document review with GPT-4 powered extraction. 85% faster processing, 98% accuracy, $2.4M in annual savings.
National insurance provider — claims and policy administration
A major Australian insurance company handling over 50,000 documents per month. Claims forms, policy applications, medical reports and correspondence — almost all processed by hand.
A team of 35 staff spent most of their day reading documents, extracting key data, and keying it into the claims management system. Error rates sat between 8–12%, turnaround was 3+ business days per document, and overtime costs were growing every quarter.
What needed to change
The volume was unsustainable. 50,000 documents a month, 47 different document types, and every one needed a human to read it, pull out the right fields, and key it into the system. Quality control meant a second person checking the first person's work.
Error rates were climbing. Compliance obligations meant extraction errors on claims or medical documents created real downstream risk. But the manual process was the error source — fatigue, inconsistency, and sheer volume were working against the team.
Turnaround was blowing out. 3+ business days per document had become the norm. Claimants were waiting longer, internal SLAs were being missed, and the operations team was burning out.
What we built
An intelligent document processing pipeline that classifies, extracts, validates and routes documents automatically — with human review only for low-confidence items.
AI Classification Engine
Automatically identifies the document type from 47 categories — claims forms, medical reports, policy applications, correspondence — using fine-tuned GPT-4 models.
Data Extraction Pipeline
Extracts key fields (policy numbers, dates, amounts, medical codes) with validation against business rules. Low-confidence extractions route to human review.
Automated Routing
Processed documents flow directly into the claims management system. The right data lands in the right record without manual data entry.
Monitoring Dashboard
Real-time visibility into processing volumes, accuracy rates, confidence scores and exception queues for operations management.
How it works
Document arrives
Documents enter via email, upload or API. The system accepts PDF, scanned images and structured forms.
AI classifies the document
The classification model identifies the document type and routes it to the correct extraction pipeline.
Data extraction and validation
Key fields are extracted and validated against business rules. Confidence scores determine whether the result is auto-approved or flagged for review.
Human review (if needed)
Low-confidence extractions surface in a review queue. Staff correct or confirm — and corrections feed back into model improvement.
Data flows into claims system
Validated data writes directly into the claims management platform via API. No manual re-keying required.
Measurable outcomes
HELLO PEOPLE transformed our document processing from a bottleneck into a competitive advantage. What used to take days now happens in minutes, and our team can focus on the work that actually requires human judgment.
How we delivered it
Discovery & Analysis
3 weeksComprehensive document audit across all business units. Identified 47 document types, mapped processing workflows, and benchmarked current processing times and error rates.
AI Model Development
6 weeksDeveloped and fine-tuned custom classification and extraction models using GPT-4, trained on real document samples. Built validation pipelines to meet compliance accuracy requirements.
System Integration
4 weeksConnected the AI pipeline to existing claims platform, document storage and reporting tools. Built APIs for seamless data flow between systems.
Testing & Validation
4 weeksRan parallel processing — AI alongside manual — for 4 weeks. Refined models based on edge cases and established confidence thresholds for human review routing.
Rollout & Training
3 weeksPhased rollout across business units with staff training. Established monitoring dashboards and feedback loops for continuous model improvement.
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