📈 Enterprise-Scale Structured Payslip Data Extraction

🎯 Problem Statement

Manual mortgage document review was a bottleneck—error-prone, labour-intensive and unable to scale—delaying approval cycles and degrading customer experience.

Led the design and delivery of a mission-critical document processing system for a financial services client, automating mortgage approvals and driving operational excellence with advanced ML.

⚙️ Technical Approach

• Modular Python microservices orchestrated by CrewAI for specialised cognitive tasks
• LangSmith integration for real-time monitoring, tracing and alerting
• RESTful APIs with FastAPI; dependency management via Poetry
• Containerised deployments on Docker and AWS for resilience and scalability
• Dual-LLM verification pipeline to cross-validate extracted data

🛠 Skills

Python microservices · CrewAI · LangChain · LangSmith · Docker · FastAPI · Poetry · Pydantic · AWS · multi-agent AI · dual-LLM verification · observability · OpenAI · Google Vertex AI

🔧 Challenges & Solutions

• Format diversity (PDF, PNG, DOCX): built a unified preprocessor and scalable image pipeline
• Ensuring data integrity: implemented multi-LLM model consensus and enforced consistent, structured outputs with Pydantic schemas
• Pipeline resilience: added retry logic and fault-tolerant workflows
• Observability & error handling: centralised logs, Slack webhooks and LangSmith dashboards
• Edge-case handling: developed adaptive learning modules for atypical document layouts

📊 Quantifiable Business Impact

• 52% reduction in processing errors
• 15% faster mortgage approval times
• 80% decrease in manual review overhead
• Sustained data-accuracy gains via dual-LLM validation
• Seamless scaling to thousands of documents per week without performance degradation

⭐ Client Review