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Almost 18 months after leaving Databricks, I’m stoked to announce that I have joined Resolve AI to lead marketing. I took the first six months to catch a breath after close to 6 years of intense experience of building at Databricks. Last year I spent balancing time with my son (currently a junior in high school) and advising founders at early stage companies on product-market fit. I had no intention of getting back to full-time company building until my son finished high school, but everything changed after I met Spiros and the team at Resolve AI.
I found it irresistible not to join the team at Resolve AI. Here’s why -
We believe software engineers are the DaVincis and Michelangelos of the new AI era. Our mission is to empower them to dream and create the future by eliminating the grind of software operations. While many companies are focused on solving the problem of code generation, we are dedicated to reimagining software production operations with Agentic AI
Before joining Resolve AI, I spent several weeks collaborating with Spiros and Mayank on the company’s launch. I witnessed their humility in how they treated every team member, their urgency driven by a massive opportunity, and their ego-free approach to seeking expertise and learning rapidly. It's no surprise the founding team—comprising the founders, engineers, and GTM—has successfully worked together across three previous startups.
We believe that the software operations market is a $100B opportunity waiting to be disrupted by Agentic AI. We are actively working with senior leaders of engineering (CTO’s and VP’s) across tech-natives seeing hyper growth and established Fortune 500 companies. Post launch the market demand and interest is unprecedented - nobody wants to be on-call or run operations, everybody wants to build and ship features. We believe we have an opportunity to increase developer productivity massively and fundamentally alter how software is managed in production.
The founding team has spent decades building developer tools for engineering teams. From day one, we have worked with strategic design partners to tackle the right problems in the right way. We are backed by Greylock - it's the largest seed check they have written for any company ever. We have angel investors ranging from AI luminaries (Fei-Fei Li, Jeff Dean), industry leaders (Matt Garman, Sridhar Ranagaswamy) backing us. We have the foundation to quickly achieve product market fit and accelerate the growth of the company.
In an early-stage company, strategy and execution are inseparable—you have to nail both while staying laser-focused on the customer. Driving world-class execution means aligning relentlessly across the mission (why), strategy (what), team (who), and day-to-day operations (how) in a constantly changing world. I experienced this firsthand at Databricks - it was simultaneously the greatest joy and the most challenging work I have done. Now I’m thrilled to take on this journey at an even earlier stage with Resolve AI alongside humble, intelligent, and ambitious folks.
Looking forward to building with you all - Spiros, Mayank and Saam.
Join our engineering leads for "Behind the Build", a webinar series deep-dive into how we built agents that run software.
The question isn't whether AI belongs in production anymore. Here's what engineers at AWS Summit NYC 2026 told us about how agents run your software, what guardrails they want, and how the pricing should work.
A frontier model can produce a thousand coherent answers. Most enterprise work needs exactly one correct one, and closing that gap is not a bigger model. It is the agent architecture around it. Here are the six layers that turn open-ended capability into a defined outcome, and why production incidents are the hardest test of whether they work.
Watch how Resolve AI investigates a service timeout from application logs through Kubernetes pods down to failing memory modules in a UCS blade - building a complete causation chain in 3 minutes. See the stark contrast between traditional multi-team incident response (4 teams, multiple tools, hours of coordination) and AI-native investigation that maps dependencies from app code to storage infrastructure without organizational handoffs. Learn why engineering silos slow incident response and how AI agents can reason across the entire production stack as one connected system.
