A technical hub for production-ready AI engineering projects. Learn best practices for building, deploying, and scaling AI systems in real-world production environments.
F1 AI Race Engineer is a full-stack AI racing analytics project that combines FastF1 data with an interactive dashboard and AI reasoning workflows. It lets users replay completed race weekends and inspect standings, tyre strategy, position changes, weather evolution, and race control events in a single interface. The platform includes race overview, race replay, telemetry comparison, and AI-generated race summary and driver insights features. It is designed as a production-style system with FastAPI backend services, a modern frontend, and configurable LLM integrations.
SentiWiki AI is an end-to-end agentic RAG system that treats documentation retrieval as an intelligent engineering problem. It distinguishes between looking up a fact (Retrieval) and deriving an answer (Reasoning), using LangGraph-based agents to route queries, decompose complex questions, and retrieve grounded answers from 66+ SentiWiki documents. The system is built as a production-grade, fully deployed stack on AWS ECS Fargate, demonstrating real-world engineering practices including end-to-end data pipelines, agentic intelligence, hybrid retrieval, asynchronous FastAPI backend, and complete observability.