Growth Without Operational Expansion
85% straight-through processing rate eliminated the need for additional headcount despite 30% volume growth.
Technologies used
Python
Azure
PostgreSQL
Apache Kafka
Docker
Kubernetes
Power BI
Motivation
The client's shared service center processed over 40,000 documents per month: invoices, claims attachments, and policy forms, using a combination of manual review and legacy OCR tools with high error rates. The goal was to dramatically reduce manual effort and SLA delays without replacing the existing ERP (SAP) or document management system. A full redevelopment was ruled out due to cost and risk. The client needed an AI layer that could integrate with what already existed, be operated continuously, and improve over time.
Main challenges
Challenge 01
Legacy System Integration Without Replacement
The client's SAP environment and document management platform were deeply embedded in operations. Any solution had to integrate via APIs and existing data connectors, not require a platform migration or core system change.
#1
Challenge 02
High Document Variability
Incoming documents came from dozens of third-party partners in inconsistent formats: scanned PDFs, email attachments, portal uploads, and EDI feeds. Standard OCR tools failed on edge cases and non-standard layouts at unacceptable rates.
#2
Challenge 03
Regulatory and Audit Requirements
As an insurance group, the client operated under strict data governance obligations. Every AI-assisted decision needed to be logged, explainable, and retrievable for audit. Human review workflows had to be preserved for low-confidence cases.
#3
Challenge 04
No Internal AI Operations Capability
The client had no dedicated ML Ops or AI engineering team. After any initial deployment, there was no internal capacity to monitor model drift, retrain on new data, or optimize API costs, making ongoing management by a third party essential.
#4
Our approach
Integration-First Architecture
Rather than building a standalone platform, we designed an AI middleware layer that sits between the document intake points and the existing SAP and DMS environment. All AI outputs flow into existing workflows via pre-built connectors.
Managed AI Operations from Day One
The engagement was scoped as Build + Operate from the start. Azati owns extraction accuracy, uptime SLA, and continuous improvement, not as optional support, but as the core delivery model.
Human-in-Loop for Compliance
Low-confidence extractions are automatically routed to a human review queue with AI-generated annotations. All decisions, automated and manual, are logged with full audit trail.
Ongoing Cost Optimization
Azati actively manages model selection, prompt efficiency, and caching to control per-document AI costs, reporting monthly on cost-per-document metrics.
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01
AI Document Intake and Classification
Automated ingestion from email, portal upload, and EDI channels. AI classifies document type, extracts structured fields, and routes to appropriate processing workflow.
Key capabilities:
- Multi-format ingestion (PDF, TIFF, DOCX, XML)
- Document type classification with confidence scoring
- Automated field extraction and validation
- Exception queue with human review interface
02
ERP Integration Layer
Extracted data is validated against master data in SAP and pushed directly into the appropriate ERP objects, purchase orders, claim records, supplier invoices, via existing API endpoints.
Key capabilities:
- SAP REST API integration
- Master data matching and deduplication
- Automated status updates and notifications
- Full rollback capability for failed records
03
Compliance and Audit Module
Every AI action is logged with timestamp, model version, confidence score, and reviewer identity where applicable. Logs are retained per GDPR requirements and exportable for regulator review.
Key capabilities:
- Immutable audit log per document
- GDPR-compliant data retention and deletion
- Role-based access control
- Regulator-ready export formats
04
Managed Operations Dashboard
Client operations team has a live dashboard showing processing volumes, accuracy rates, exception queue status, and cost-per-document. Azati reviews metrics monthly and adjusts models accordingly.
Key capabilities:
- Real-time processing metrics
- Accuracy trend tracking
- Cost-per-document reporting
- Monthly performance review with Azati team
Results & business impact
Significant Cost Savings
Cost per processed document reduced by 52% within six months of go-live.
Near-Instant Processing
Average processing time dropped from 2-3 business days to under 90 seconds.
Audit-Proof AI Operations
Full audit trail enabled the client to pass two regulatory reviews with zero findings related to AI processing.
Transformation Without Disruption
Zero changes to the core ERP or document management platform, the AI layer was integrated, not a replacement.
