Resolve AI Secures $125M Series A at Unicorn Valuation to Automate System Reliability Engineering

AI SRE Resolve AI confirms $125M raise, unicorn valuation | TechCrunch

Resolve AI, a startup automating the work of system reliability engineering (SRE), aka troubleshooting system failures, has announced a $125 million Series A at a $1 billion valuation. The round was led by Lightspeed Venture Partners, with participation of existing investors including Greylock Partners, Unusual Ventures, Artisanal Ventures, and A*. The announcement confirms TechCrunch's December report that the startup was raising at a billion-dollar valuation led by Lightspeed. Sources told TechCrunch at the time that the round may have consisted of multiple tranches, at different prices, which could have put the company's actual blended valuation below $1 billion. A spokesperson for Resolve denied that there were multiple tranches in the round, saying that 100% of the equity was purchased at a valuation of $1 billion. As we previously reported, this kind of structure allows certain investors, often the lead, to purchase a significant portion of equity at a lower price. Resolve was co-founded in early 2024 by two former Splunk executives, Spiros Xanthos and Mayank Agarwal. Their previous startup, Omnition, was acquired by Splunk in 2019. Another startup applying AI to identify and resolve system outages is the Sequoia-backed Traversal. The emerging category is known as AI SRE.

Resolve AI Hits $1 Billion Valuation for Outage-Thwarting AI Agents

Resolve AI, a startup building AI agents to find and fix problems in live software systems, just hit a $1 billion valuation in a new funding round. The company has raised $125 million in a deal led by Lightspeed Venture Partners, the startup plans to announce Wednesday. Existing investors Unusual Ventures, Artisanal Ventures and A* also participated, along with Greylock Partners, which led the startup's $35 million seed round in late 2024. Resolve AI launched from stealth in late 2024 and has since signed on more than 20 customers, according to the startup, including big names like Salesforce Inc., Coinbase Global Inc. and DoorDash Inc. For those companies, going offline even for a minute can be extremely costly, said Resolve AI Chief Executive Officer Spiros Xanthos. The startup's software aims to limit downtime by monitoring customer-facing systems automatically resolving software problems. Co-founders Xanthos and Chief Technology Officer Mayank Agarwal began their careers as developers. The two started Resolve AI after leaving Splunk, the data platform Cisco Systems acquired in March 2024 for $28 billion. (Splunk had acquired Xanthos and Agarwal's prior company, Omnition, in 2019.) As developers, Xanthos and Agarwal spent about 80% of their time maintaining tools that were already live with customers rather than building new features, Xanthos said. Get the Tech Newsletter bundle. Get the Tech Newsletter bundle. Get the Tech Newsletter bundle. Bloomberg's subscriber-only tech newsletters, and full access to all the articles they feature. Bloomberg's subscriber-only tech newsletters, and full access to all the articles they feature. Bloomberg's subscriber-only tech newsletters, and full access to all the articles they feature. Bloomberg may send me offers and promotions. Plus Signed UpPlus Sign UpPlus Sign Up By submitting my information, I agree to the Privacy Policy and Terms of Service. Resolve AI aims to offload much of that job to artificial intelligence agents, which are software programs that can take actions autonomously. The startup's tech keeps tabs on software systems -- including the source code, connected databases and underlying infrastructure. When something breaks on the front end, the agents can find the issue's root cause and resolve it automatically, Xanthos said, reducing downtime without requiring engineers to be on call to manually intervene. The agents also help keep the system healthy and secure, he said, flagging potential vulnerabilities and performance degradation. Resolve AI is part of a growing wave of companies applying AI to software development. While buzzy coding agents like Cursor or Claude Code can help developers generate new code much faster, Resolve AI focuses on software that's already working. The startup uses frontier AI models as well as its own in-house models to build its AI agents. Xanthos said Resolve AI plans to use the new capital infusion to continue investing in its own models, accelerate its go-to-market strategy and hire more engineers. Fierce competition for AI talent could complicate recruiting. But Xanthos said that the startup has been able to pull from top labs, noting that 14 of its some 120 employees hail from Google's DeepMind. Many of the startup's prospective employees also have their own experiences with software production. "Each one of them has probably dealt with stressful, tedious production-related work," Xanthos said. "So these people understand the problem, it's personal to them, and they understand the impact this solution can have," he said.

Resolve AI raises $125M at $1B valuation to automate application maintenance - SiliconANGLE

Resolve AI raises $125M at $1B valuation to automate application maintenance Resolve AI Inc., a startup with a platform that makes it easier to fix issues in production applications, has closed a $125 million funding round at a $1 billion valuation. Lightspeed led the Series A investment. Resolve AI said in its announcement of the deal today that Greylock, Unusual Ventures, Artisanal Ventures and A* participated as well. The company previously raised $35 million from a consortium that included prominent artificial intelligence researchers Fei-Fei Li and Jeff Dean. The root cause of an application outage isn't always immediately apparent. Downtime can be caused by one of the application's components, the external database in which it keeps information or the underlying hardware. Developers have to evaluate each possibility one by one, which can take a significant amount of time. Resolve AI's platform uses AI to speed up the task. The software identifies the potential causes of a malfunction and creates AI agents to investigate each one. Those agents work in parallel, which is faster than reviewing the potential root causes one after one another. The platform collects the technical data necessary to carry out troubleshooting by connecting to a development team's other tools. It can integrate with monitoring applications, code repositories, cybersecurity software and other sources of information. Resolve AI assembles the data points it collects into a regularly updated map of production applications' components. According to the company, its platform can not only find the root cause of a malfunction but also help developers fix it. The suggestions that the software generates vary based on the issue. If a container cluster goes offline, Resolve AI may generate commands that can be entered into kubectl, a tool that developers use to manage Kubernetes. The platform is also capable of generating configuration scripts and updates to application code. Resolve AI says that fixing outages isn't the only use case supported by its platform. Engineers can use a built-in chatbot to study the architecture of an application environment and identify potential future situations that may cause it to experience issues. Furthermore, the platform identifies opportunities to reduce infrastructure costs. Resolve AI will use the proceeds from its Series A round to grow its go-to-market team. In addition, the company plans to develop custom AI models that will enable its platform to automate more tasks for users.

Resolve AI Raises $125 Million for AI Agents That Maintain Software | PYMNTS.com

By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our Privacy Policy and Terms and Conditions. The round brings the company's total funding to over $150 million and its valuation to $1 billion, Resolve AI Founder and CEO Spiros Xanthos said in a Wednesday (Feb. 4) blog post. Resolve AI's "AI for prod" serves as an AI site reliability engineer (SRE) that can work alongside human engineers and SREs and handle production work such as incident diagnosis, rollback decisions, capacity adjustments, configuration changes, infrastructure actions and guided code changes, according to the post. Since its launch about a year ago, this solution has been deployed by technology, financial services and consumer application companies. One client found that it reduced the time to investigate critical incidents by 72%, while another saw it reduce the number of engineers required per incident by 30%, the post said. With the new funding, Resolve AI will focus on research and development to stay at the forefront of agent development and model training, product depth to expand integrations across the production stack, and customer success to support growing global enterprise deployments, per the post. "The agent era will create far more software than any era before it," Xanthos said in the post. "The teams that win won't be the ones that write code the fastest. They'll be the ones who can run what they write, reliably and securely, at the same pace." "That's what AI for prod enables, and this Series A allows us to keep building it," Xanthos said. Sebastian Duesterhoeft, partner at Lightspeed Venture Partners, which led the round, said in a Wednesday post on LinkedIn that the bottleneck in tech is no longer building software but maintaining it. At many companies, engineers spend as much as 80% of their time running and maintaining software. "And as AI accelerates how much software gets written, this will only get harder," Duesterhoeft said in the post. "More code creates more complexity, more incidents, and slower progress. This is the problem Resolve AI is built to solve." PYMNTS reported in December that enterprise AI is entering a new phase as companies shift from experimenting with large language models to moving those systems into live environments. This has caused a shift in investment and engineering resources toward inference infrastructure.