San Francisco–based Resolve AI has raised $125 million in a Series A round, pushing the company’s valuation to $1 billion and placing it among the latest AI unicorns focused on enterprise software reliability.
The funding round was led by Lightspeed Venture Partners, with strong participation from existing backers Greylock Partners, Unusual Ventures, and Artisanal Ventures. The round brings Resolve AI’s total funding to more than $150 million.
The company said the fresh capital will be used to accelerate product development, expand its engineering and go-to-market teams, and support rising demand from large enterprise customers.
What Resolve AI does
Founded by observability pioneers Spiros Xanthos and Mayank Agarwal, Resolve AI is building what it calls an AI-powered production engineer for modern software teams. The founders were early contributors to OpenTelemetry and previously built and sold companies to Splunk and VMware.
The US company focuses on autonomous Site Reliability Engineering (SRE). Its platform monitors complex cloud environments such as AWS and Kubernetes, investigates production incidents, finds root causes, and recommends or automatically executes fixes.
Instead of relying on static rules, the system builds a live knowledge graph of how a company’s infrastructure actually works and uses AI agents to reason across logs, metrics, deployments, and configuration changes.
“In most companies, the hardest part of software engineering isn’t writing code. It’s running production,” said co-founder Spiros Xanthos. “We started Resolve AI a little over a year ago to help engineers debug and operate production systems. Today, our agents are already running in production at some of the world’s largest technology and financial services companies.”
Why does the problem matter?
As software systems grow more complex, outages often span multiple tools and services. Diagnosing issues can pull large teams into long incident calls, slowing down development and increasing costs.
The company aims to reduce this operational burden by letting AI agents handle much of the investigation work while keeping humans in control of final decisions.
According to the company, customers are already seeing a measurable impact. Coinbase has reported a 72% reduction in time spent investigating critical incidents, while Zscaler has reduced the number of engineers required per incident by 30%.
Other customers include DoorDash, Salesforce, MongoDB, and MSCI.
What’s next?
The US company plans to focus on three main areas with the new funding: advancing its AI research for software engineering, deepening product capabilities across the production stack, and expanding customer support for global enterprise deployments.
Today, the platform works alongside engineers in tools like Slack, Microsoft Teams, and the terminal. The long-term goal is to help prevent production issues before users ever notice them.
“Our belief is simple,” said Agarwal. “The teams that win in the AI era won’t just be the ones that ship code fastest. They’ll be the ones who can run what they build reliably at scale. This funding allows us to keep pushing toward that future.”
