I build AI operations and data platform products that help companies ship AI to production reliably. Currently VP Product at InfinyOn, where I'm leading our pivot to CoAgent - a logging, testing, and monitoring system for Gen AI applications and Agents that gives teams confidence to deploy AI agents and workflows to production.

The problem we're solving is critical: 95% of AI projects fail to reach production not because of model issues, but because teams lack operational visibility. CoAgent gives engineering teams the testing and monitoring infrastructure they need to answer "Why did my agent fail?" before their customers do.

Over the past decade, I've built products that drove real business impact:
- 5K+ GitHub stars and hundreds of thousands of users (InfinyOn - Fluvio open source)
- $5K MRR with first 6-figure enterprise deal (InfinyOn - Stateful Dataflow)
- 3 enterprise pilots in 6 months (CoAgent)
- $7M+ pipeline in first year (Jungle Scout - Data Brokerage product)
- $100M+ annual cost savings (GM - AI insurance claims automation)

My approach combines deep technical understanding with product discipline. I speak fluent product, data, systems, and AI/ML/DL. I'm equally comfortable in enterprise sales calls, defining GTM strategy, and building product-led growth motions. I've led teams from seed stage (employee #7 at Kovid Labs) to Fortune 50 (GM's 165K+ employees).

Before focusing on AI operations, I spent years in the trenches building AI/ML systems across regulated industries: surveillance tech (Kovid Labs), pharmaceutical R&D (DataFoundry), automotive manufacturing (GM), and eCommerce intelligence (Jungle Scout). This cross-industry experience taught me that production AI is less about fancy models and more about operational excellence.

Right now, I'm obsessed with making AI operations boring in the best way - reliable, observable, testable. The same way CI/CD made web deployments boring 15 years ago, we're doing that for AI.

My technical foundation includes Computer Science Engineering and hands-on experience across the full stack - from data collection and labeling to model training to production deployment.