Hi, I'm Sak Nagpal

Built and deployed a production LLM-based customer complaint classification and routing system processing 100k+ historical enquiries at Jetstar Airways. Combined the Claude API with KNN clustering to identify complaint categories, achieving 92% classification coverage — replacing a brittle rule-based approach and materially improving routing accuracy and resolution efficiency. Designed a RAG-based architecture integrating internal knowledge sources, evaluating trade-offs across latency, cost, and model performance.

Designed and delivered an NLP pipeline for automated classification of aircraft maintenance and engineering logs at Jetstar Airways, where unclear technician-written entries previously required manual expert review. Extended the system to forecast recurring fault patterns across the fleet — reducing mean time to identify faults by 30% and enabling proactive maintenance scheduling. The system handles noisy, domain-specific language with high accuracy across a diverse range of fault types.

Architected and built an internal LLM-powered knowledge assistant using Amazon Bedrock, trained on operational manuals and staff travel policy documents. The system enables accurate, instant retrieval of policy information — reducing staff onboarding time by over 95% with near-100% accuracy in seat allocation decisions. Built on a RAG architecture with careful chunking and retrieval strategies optimised for long-form policy documents. Currently in final pre-production validation.

A full-stack agentic AI application that researches Melbourne suburbs for property investment decisions, built on a LangGraph ReAct reasoning loop with a FastAPI streaming backend and React frontend.