The Story

I started my career designing transmission components at Maruti Suzuki, India's largest automaker. Watching parts move from design through supplier qualification into mass production gave me my first real mental model of how design, sourcing, and production actually chain together. I was later transferred into supply chain to help drive the company's inner-part localization program — bringing technology-heavy imported parts in-country as yen-policy shifts made imports too expensive. Between those two chapters, one lesson cemented itself: supply chains aren't logistics problems — they're complex adaptive systems. It's shaped everything I've built since.

I left the shop floor for a Master's in Industrial Engineering at Oklahoma State University — diving into optimization, discrete-event simulation, and operations research. Graph algorithms, facility location, vehicle routing: the mathematics of how decisions at one node ripple across an entire network. This was where the systems-thinking instinct got its technical vocabulary.

From 2017 to 2025, I was at Applied Materials, working in the semiconductor equipment supply chain — where a single hour of fab downtime costs six figures. The work spanned two chapters: first in logistics network optimization, redesigning routing and duty flows to keep service levels high as trade policy shifted; then in spare parts planning, where I built simulation models and machine learning forecasts to put scenario planning in front of critical decisions. Every project was a lesson in how analytics, without the right organizational process behind it, doesn't actually move the needle.

Around late 2024, I started using AI coding tools to compress the distance between idea and shipped system. What began as efficiency experiments turned into something bigger: agentic AI isn't just a better way to code — it's a fundamentally different way to run a business process.The routine decisions a human planner used to make — the ones where judgment matters but creativity doesn't — can be handled by well-designed agents with the right guardrails.

That's what I'm building now — agentic AI systems for business process automation, starting with supply chain but not limited to it. The work spans BPMN-driven workflow orchestration, MCP-based integrations, and production microservices for demand forecasting and inventory optimization. I'm interested in where this goes next — whether that's deploying these systems inside an organization, advising on an agentic strategy, or hearing what you're working on.