Introducing Resolve AI

Resolve AI is launching today to empower engineers with AI tools that automate software operations. Our first product acts as an AI Production Engineer, which autonomously troubleshoots and resolves production issues and handles operational tasks, dramatically reducing MTTR and freeing up engineers to focus on building. By combining a deep understanding of production environments with state-of-the-art agentic AI, we aim to accelerate how engineers build, deploy, and maintain real-world software systems.

We’re also pleased to announce our $35M Seed round, led by Greylock with participation from Unusual Ventures and an exceptional group of angel investors.

Engineers spend the majority of their time dealing with operational tasks

Conversations with hundreds of executives and engineers have revealed a common pattern: in real-world software systems, engineers spend the majority of their time on operational tasks–on-call, troubleshooting, infrastructure management and security–that require a deep understanding of code and production environments. Accomplishing these requires combining knowledge from multiple tools that were not designed to work together, pushing complexity onto humans and making the work challenging and time-consuming.

Our experiences are similar. When I led Splunk’s Observability business, 90% of the SRE team resigned within six months due to burnout from on-call duties. Most customer escalations were due to reliability issues, and at times, production was frozen for months to avoid outages, halting innovation.

AI coding assistants are accelerating coding and feature development but this will also make this operational complexity worse. We can't go faster until we solve these operational bottlenecks.

Resolve AI Improves MTTR and productivity in real-world systems

To address this complexity, we are building the first AI that deeply understands production systems and can help operate them. As a first step toward our ambitious goals, we automate on-call incident troubleshooting and remediation—not only a very stressful task for engineers, but also the most direct way to prevent outages and improve reliability.

Built in close collaboration with our customers, Resolve AI is now deployed in several production environments, delivering real-world impact for engineering teams. It reduces Mean Time to Resolve (MTTR) significantly, increasing uptime and productivity.

Machines running production systems

Resolve AI today is a state-of-the-art agentic AI system that already understands source code, telemetry, cloud infrastructure and services, and can reason about novel tasks and incidents. It uses tools like AWS, Kubernetes, GitHub, and Slack—just like a human engineer—and resolves alerts and incidents in seconds. It can answer questions about the health of the environment, dependencies, changes by joining data across observability tools, infra, code and release pipelines. Our aim is to auto-resolve 80% of alerts and incidents without human intervention.

More operational tasks that require deep understanding of source code and production combined with agentic AI, like prevention of incidents, cloud cost optimization and source code changes, are a natural extension. Over time, we envision allowing engineers to operate at a higher level of abstraction, overseeing AI that runs production systems. This shift will enable teams to achieve greater velocity and efficiency in software development and operations.

The people behind Resolve AI

Mayank and I met 20 years ago in grad school at UIUC and have been working together since 2012. Since then, we've built multiple generations of development and observability tools in startups and large companies, and created impactful open-source projects like OpenTelemetry. Also, this is the third time five of our founding team members are working together but we also added many new teammates that bring diverse experiences and new skills. We are driven by the same mission of empowering engineers with AI tools that will help them shape the future and we are all dedicated to building a company that will have a lasting and positive impact on the world.

Our customers are central to how we build Resolve. Innovation means nothing without solving real problems, which is why we've worked closely with a group of visionary customers– like DataStax, Uni, Blueground and more–from day one. That enabled us to move quickly and make Resolve AI production-ready in under six months. I'm grateful for their invaluable insights and generous feedback that shaped our product.

We are grateful to our investors for their support in building the company. Our $35M Seed round was led by Saam Motamedi at Greylock, with participation from John Vrionis at Unusual, and a group of AI and technology pioneers, from companies and institutions such as AWS, Google, Github, Replit, OpenAI, Snowflake and Stanford, including: Paul Daugherty, Jeff Dean, Thomas Dohmke, Matt Garman, Eric Glyman. Reid Hoffman, Colin Jones, Akshay Kothari, Christos Kozyrakis, Jeff Lawson, Fei Fei Li, Amjad Masad & Michele Catasta, Srinivas Narayanan, Andy Price and Sridhar Ramaswamy

Work with us

See Resolve AI in action or request access by booking a demo. If you’re passionate about what we’re building, consider joining our team.

Let’s get back to building!