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.
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