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 →Launched "VizData"
VizData allows you to visualize data from databases or spreadsheets to uncover insights and patterns.
Supabase and a relook at vendor risk
Vendor risk Is not just about outages or pricing A few weeks ago I ran into an unusual issue. A few of my projects stopped working overnight. The reason was unexpected. Supabase had been blocked in India. There was no warning and no migration window, something I had neither anticipated nor planned for. The incident was another reminder of how unpredictable the environment around software systems can be. The biggest learning for me was how vendor lock-in can introduce unexpected risk. Today it was Supabase, but the same thing could happen with an LLM provider, a cloud platform, or any other critical service in a project. A ban is just one scenario. Vendors shut down, get acquired, change pricing, or introduce breaking changes in their ecosystem.
Chaos is a Ladder, not a Pit
The markets are currently overreacting to every little update from major AI labs. Neither is the SaaS story over, nor are IT companies being destroyed already. Furthermore, the success of agentic coding in controlled environments does not mean software engineers are no longer needed. Recent developments have primarily created chaos. Enterprises are unsure how to navigate this shift. They need help and guidance through this, the companies best positioned to advise them are those that have already invested in understanding their specific niches, architectures, and current challenges, not because they have all the answers, but because they know which questions actually matter. Extending that expertise to help them integrate emerging AI technology is the most sensible path forward. While the shift is massive and sudden, the software component of SaaS may look fundamentally different in a few years. But the underlying business problems it solves will remain. Services provided by IT firms will adapt to new opportunities around that.
The QA Shift for probabilistic systems
With the rapid progress of coding agents, getting a working prototype or MVP out quickly has become significantly easier, even for small teams. At the same time, baseline expectations for software have risen sharply. What felt like a delighter in 2023 barely meets expectations today. And that is assuming the product avoids the usual blind spots. When building a product meant for real business users, these challenges cannot be treated as afterthoughts. In finance, legal, healthcare, or enterprise workflows, failures are expensive. Traditional software breaks and throws errors. AI breaks differently: confident nonsense, risky actions, silent data leaks. That is a fundamentally different failure mode. Classic QA assumed determinism, but binary pass/fail logic no longer holds. You are evaluating behavior quality, not just code correctness.
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.