Product Manager & Product Owner based in Hyderabad, India, building across B2B SaaS, enterprise, telecom and AI — from discovery to release and beyond.
Currently building AI-powered product management systemsEach one started with a different kind of ambiguity — and ended with a sharper decision.
One intelligent layer across product, engineering, QA and analytics — built to surface what needs attention before it becomes a meeting.
Making a complex enterprise experience more legible for the teams and customers who depend on it every day.
Release quality issues and scattered feedback made it hard to see what deserved attention.
Bring together customer friction, support patterns, delivery signals and stakeholder context to find the smallest high-confidence intervention.
Prioritize clarity in the end-to-end journey over a broad redesign that would add delivery risk without resolving the core customer problem.
Product as connective tissue — aligning engineering, QA, architecture, business and customers around an explicit decision and a measurable release plan.
40% fewer post-release defects and 38% fewer inbound support inquiries.
Build earlier feedback loops around the riskiest assumptions, and keep more qualitative customer context alongside the operational metrics.
The loop I run every initiative through. Pick a stage to see the questions, artifacts and measures behind it.
Prototypes and systems I’m exploring before they become something larger.
Product signals stitched together from the tools teams already use.
A lightweight way to find recurring customer friction across channels.
A decision aid connecting scope, risk, QA and customer impact.
Ownership, change and lessons — not a list of duties. Education & learning →
Working across product strategy, discovery, delivery, AI and customer experience to turn complex needs into shippable decisions.
Built product judgment by partnering closely with cross-functional teams, translating needs into a clear path from problem to release.
Practical notes on building products. All notes →
An agent earns trust by making its evidence, confidence and unanswered questions visible — not by summarising more data.
Behind a familiar request is often a workflow confidence problem, not a settings-page requirement.
Planning systems should leave enough room for evidence to revise a plan without breaking trust.
Where I share product thinking on AI, B2B SaaS and discovery. Follow along for new posts.
AI Product Manager | Building Agentic AI & SaaS Products from 0→1 | Product Strategy • AI Automation • Workflows • GTM & Growth | Harvard’s HPAIR ACONF
Have a product challenge, a role, or an idea worth pressure-testing? I’d love to hear about it.