Intelligent Process Automation for Gulf Enterprises

We designed and deployed document processing pipelines combining Azure Document Intelligence for OCR with GPT-4 for semantic extraction. The challenge wasn't just reading text — it was understanding context across two languages with different reading directions, mixed-language fields, and inconsistent formatting across hundreds of form variants. The pipeline processes documents in three stages: layout analysis identifies form fields and their spatial relationships, OCR extracts text with language detection, and GPT-4 interprets the extracted data against a schema, handling edge cases like handwritten annotations, stamps, and corrections. We achieved 94% first-pass accuracy, with a human-in-the-loop review stage for the remaining 6% that continuously feeds corrections back into the model.

For the insurance client, we built predictive models using scikit-learn that score incoming claims for fraud risk based on 47 features extracted from claim data, policy history, and behavioral patterns. The model identifies suspicious patterns — claims filed within days of policy inception, duplicate claims across related policies, and inconsistent damage descriptions — and flags them for investigator review. The system reduced fraudulent claims processing by 35%, catching patterns that human reviewers consistently missed due to volume. False positive rates were kept below 8% to avoid creating bottlenecks in legitimate claims processing.

We deployed LLM-powered customer service chatbots using LangChain with retrieval-augmented generation, connected to each client's knowledge base. The chatbots handle 60% of inquiries without human intervention — policy questions, claim status updates, coverage explanations, and document submission guidance. The key to adoption was accuracy and escalation design. When the chatbot's confidence drops below threshold on sensitive topics (claim disputes, coverage denials), it seamlessly hands off to a human agent with full conversation context. We implemented continuous learning loops: human agent responses to escalated conversations are reviewed and fed back into the knowledge base, improving the chatbot's accuracy over time.