Ground explanations in inspectable evidence and clear limitations.
About
Building useful AI systems with visible boundaries.
Naman Manocha is an AI engineer focused on GenAI, agentic systems, retrieval, and applied machine learning.
The work combines Python and FastAPI backends, Next.js interfaces, data pipelines, and model-assisted workflows. The common thread is practical: make the output inspectable, make limitations visible, and keep people in control of consequential actions.
Working principles
Use deterministic systems around probabilistic model behavior.
Keep human approval gates around consequential actions.
Ship end-to-end systems while measuring what is actually working.