LLM-Powered Document Automation for Financial Services
A multinational financial services organisation processing 12,000+ documents monthly across 6 languages needed to eliminate manual extraction bottlenecks and integrate structured output directly into SAP FI/CO. ATMA Technology designed and deployed a production-grade LLM pipeline in 14 weeks.
Business Challenge
Manual processing at scale was creating systemic risk
The client's back-office operations team was processing loan applications, KYC documents, and regulatory filings entirely by hand. Volume had grown 3x in two years while headcount remained flat, creating a compounding backlog that directly impacted credit approval SLAs and regulatory reporting deadlines.
Previous RPA attempts had failed due to format variability across document types and the inability of rule-based systems to handle unstructured text, handwritten annotations, and multi-language content within the same workflow.
ATMA's Approach
A production-grade LLM pipeline built for enterprise compliance
Discovery & Data Audit
Catalogued 18 months of historical documents, identified 23 distinct document types, and established ground-truth extraction benchmarks across a stratified 2,000-document sample.
LLM Pipeline Architecture
Designed a multi-stage pipeline: OCR pre-processing via Azure Document Intelligence, semantic classification with a fine-tuned embedding model, structured extraction via GPT-4o with schema-constrained output, and confidence scoring with human-in-the-loop escalation.
SAP Integration & Validation
Built a bidirectional integration layer connecting the LLM output to SAP FI/CO via SAP BTP. Implemented field-level validation rules and a reconciliation dashboard for exception handling.
Governance & MLOps
Deployed a full MLOps framework: model versioning, drift detection, automated retraining triggers, and a compliance audit log meeting GDPR and internal risk management requirements.
Technical Architecture
Cloud-native, auditable, and SAP-integrated
The solution runs on Azure Kubernetes Service with auto-scaling configured to handle peak document ingestion loads. All LLM calls are routed through Azure OpenAI to ensure data residency compliance within the EU.
A vector store (pgvector on PostgreSQL) enables semantic search across the document corpus for audit and exception review. The SAP BTP integration layer maps extracted fields to SAP FI/CO posting schemas with full reconciliation logging.
Technology Stack
"ATMA Technology delivered a solution that our internal team had been trying to build for two years. The LLM pipeline went live in 14 weeks and immediately transformed our back-office operations. The SAP integration was seamless and the compliance audit trail gave our risk team full confidence from day one."
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