Exploring the boundaries of Artificial Intelligence and Product Management.
Experimenting and documenting AI ideas into clean, practical, scalable products.
Curiosity. Collaboration. Creativity.
With 17+ years in product, I now dedicate my personal research to the intersection of LLMs, automation, and full-stack AI systems.
I have been obsessed with AI for the last five to six years, with a strong focus on generative and agentic AI in the last two to three years. I thrive in complex environments, turn ambiguity into clear product decisions and drive teams from first concept to launch. I focus on shipping products that scale, earn trust and create measurable impact.
Product Vision
17+ years of building. I bridge the gap between abstract business goals and concrete technical execution.
UX Obsession
User-first approach. I believe AI should feel invisible, intuitive, and relentlessly helpful.
Execution
From zero to revenue. I don't just strategize; I ship products that scale and solve real problems.
Background & Learning
Trained in computer applications and management, with postgraduate education from Symbiosis University, Pune, and IIM Lucknow. Over time, formal education has evolved into continuous learning through building products, experimenting with AI systems, and leveraging modern online learning platforms.
Learning Stack
Applied AI and product systems, learned through building, experimentation, and continuous iteration on real problems.
Recent activity
All updates →Pushed to neetishtewari/superfit
Committed code (+8 more updates)
Launched "Superfit"
Superfit is an AI-powered, privacy-first Android health companion with speech-to-text nutrition logging and telemetry aggregation.
AI enablement shift
Been part of a few AI enablement discussions lately. Teams are excited about speed. Shipping faster, building faster, iterating faster. Speed is real. But I think it's the wrong thing to be excited about. When execution gets cheap, the bottleneck moves. It doesn't disappear. It moves to how clearly you can think about the problem. A vague idea used to get lost somewhere in the build cycle. Now it shows up immediately in the output and you have to confront it.
Debugging mental model for agents
Switching models to fix your agent is like tweaking your macros on 4 hours of sleep. It won't give the desired results. When something breaks, the first instinct is to switch models. GPT to Claude. Claude to Gemini It still gives similar results, often underwhelming.
My AI Research & Capability Lab
Exploring the intersection of Generative AI and Product Strategy.
AI Strategy & Workshops
I document frameworks for helping teams bridge the gap between AI hype and practical business application. My research focuses on how organizations can move from curiosity to implementation through structured discovery.
Product Audits & ROI Analysis
I experiment with methodologies to audit workflows and data structures. My goal is to identify where Agentic AI and automation can deliver measurable impact and solve "The Risk of Not Investing" (RONI).
Rapid Prototyping (MVPs)
I build "proof-of-concept" products to explore how quickly a founder's vision can be turned into a functional, scalable AI-powered tool. This is my playground for testing speed-to-market strategies.
Automation Engineering
I design and share internal experiments focused on AI-driven workflows across documents, finance, and operations, pushing the boundaries of what invisible, intuitive AI can achieve.