Pradip N.

I build and advise on real-world AI products, with a focus on product direction, architecture, evaluation, reliability, cost, and production deployment. My work spans LLM applications, RAG, AI agents, NL2SQL, document intelligence, AI infrastructure, evals, model selection, and AI-native engineering workflows. I have spent the last several years building production AI systems across startups and businesses, from early product decisions and architecture to deployment, optimization, and scaling. What I can help with AI product architecture and technical strategy Reviewing an existing AI product or implementation RAG and document intelligence systems AI agents and agentic workflows NL2SQL and structured-data applications LLM evals and benchmarking Model selection and routing Accuracy, latency, and cost optimization Production AI infrastructure AI-native development workflows Identifying unnecessary complexity and simplifying AI systems I am most useful when you already have something you are building and need an experienced second opinion on what approach to take, what to change, or what to prioritize next. Technical background Python, FastAPI, PostgreSQL, Redis, AWS, Docker, LangGraph, LangChain, OpenAI, Anthropic, vector databases, RAG pipelines, evaluation systems, APIs, and production backend systems. I am currently focused heavily on understanding and testing how AI products, models, and tools perform in real-world use cases, alongside building AI products through FutureSmart AI and AI Demos. For consulting, you can book a focused session where we go directly into your product, architecture, technical challenges, and decisions.