SAN FRANCISCO, April 16, 2026 /PRNewswire/ — Resolve AI, the AI for running and operating software in production, today announced it has raised $40 million in its Series A Extension at a $1.5 billion valuation, led by DST Global and Salesforce Ventures. Just 18 months after emerging from stealth, Resolve AI has raised more than $190 million and serves enterprise customers, including Coinbase, DoorDash, MSCI, Salesforce, and Zscaler. The company also announced the launch of the Resolve AI Labs, a strategic investment in building the domain-specific models and agentic systems required to operate complex production environments.
“We’re honored to partner with Spiros, Mayank, and the entire Resolve AI team to support their vision of bringing AI to production environments. With their extremely high talent density and decades of experience in the industry, this team is best positioned to win in leveraging AI to operate complex systems at scale,” said Rahul Mehta, Co-founder and Managing Partner at DST Global. “What stood out to us about Resolve AI is their focus on the model, data, and systems work required to make AI truly effective in production.”
The Resolve AI Labs will be led by Dhruv Mahajan, who joins Resolve AI as Chief AI Scientist. Before joining Resolve AI, Dhruv was part of Meta, where he led post-training efforts for large-scale Llama foundation models. At Resolve AI, he will apply that experience to building the domain-specific models, evaluation systems, and agentic architectures required for AI to operate reliably in production environments.
“Foundation models are improving quickly, but they are still not enough for production operations,” said Spiros Xanthos, Founder and CEO of Resolve AI. “Production environments demand reasoning over fragmented telemetry, long-running workflows, constantly changing systems, and a very high bar for accuracy. We are forming the AI Labs because closing that gap requires domain-specific models, post-training, and agentic systems designed specifically for this domain.”
General-purpose models were not built for the realities of production operations. AI systems in this domain must reason across noisy telemetry, complex dependencies, and multi-step workflows where mistakes carry real consequences, while also meeting strict requirements for accuracy, latency, reliability, and control.
The Resolve AI Labs will focus on building the model and agentic foundations required for AI to manage production systems, including:
- Domain-specific model building and post-training for production operations
- AI reasoning across operational telemetry, including logs, metrics, traces, infrastructure events, and change history
- Evaluation frameworks for measuring reliability and accuracy in real operational workflows
- Synthetic data generation and simulated environments for scalable evaluation, training, and model improvement
- System architectures for scalable operational AI
- Governance and guardrails for AI operating in production environments
“As early investors in foundation models, we’ve seen firsthand how AI is reshaping how software gets built,” said Zak Kokosa, Principal, Salesforce Ventures. “However, managing that software in complex production environments remains one of the hardest problems in enterprise engineering. It requires deep domain expertise layered on top of frontier AI, which is exactly what Resolve AI has pioneered. With a world-class team and proven traction among global enterprises, Resolve AI is uniquely positioned to lead the next phase of agentic AI operations. We are thrilled to partner with Spiros, Mayank, and the entire team.”
“Running software at enterprise scale means production incidents can have significant costs in engineering time, customer trust, and business continuity. Resolve AI has changed how our teams work through them,” said Meir Amiel, President, Chief Trust and Infrastructure Officer, Salesforce. “What used to take hours of manual investigation and coordination across teams now gets resolved in a fraction of the time. Our engineers aren’t only faster, they’re focused on the work that actually drives impact.”
The Resolve AI Labs will carry out this work in close collaboration with leading enterprises operating some of the most complex and business-critical production environments in the world. These environments generate a wide range of signals, evolve constantly, and require domain-specific reasoning and operational accuracy that off-the-shelf models cannot reliably provide. Resolve AI will use these real-world challenges to shape the models, post-training methods, and evaluation systems needed for AI to perform effectively at scale in production.
This work aims to enable production systems that can be increasingly managed by AI, in which models and agents work together to investigate incidents, diagnose root causes, and take action, with human involvement determined by risk and operational context.
“Production systems are noisy, incomplete, and constantly changing,” said Dhruv Mahajan, Chief AI Scientist at Resolve AI. “Building AI that works in those environments requires advances in model building, reasoning, evaluation, and control systems. The opportunity is to take what foundation models make possible and turn it into systems that are actually accurate, reliable, and operationally useful in production.”
This new funding will support continued investment in Resolve AI’s platform, go-to-market expansion, and long-term research initiatives, including the AI Labs, as the company builds toward AI systems capable of taking on more of the work required to run production environments.
About Resolve AI
Resolve AI is AI for running and operating software in production, which underpins how businesses operate. Founded by observability pioneers Spiros Xanthos and Mayank Agarwal, Resolve AI combines custom AI models, production-specific agents, and deep systems expertise to solve the hardest problems in modern software operations. Leading companies, including Coinbase, DoorDash, MongoDB, MSCI, Salesforce, and Zscaler, rely on Resolve AI to manage their production environments. Learn more at resolve.ai.
FAQ
What is Resolve AI announcing?
Resolve AI is announcing two things:
- a Series A Extension of $40 million at a $1.5 billion valuation, led by DST Global and Salesforce Ventures
- the launch of Resolve AI Labs, focused on building the domain-specific models and agentic systems required to operate complex production environments
Together, these reflect the company’s momentum and its investment in the next phase of AI for production operations.
Why raise this funding now?
Our Series A was oversubscribed, and the Series A Extension allows us to bring on strategic partners in DST Global and Salesforce Ventures while investing more aggressively across product development, go-to-market expansion, and long-term research, including Resolve AI Labs.
Resolve AI is also seeing strong adoption among enterprise engineering teams, and this funding gives us more room to expand ahead of a large and fast-moving market opportunity.
Why is Resolve AI launching Resolve AI Labs?
Foundation models have advanced rapidly, but there is still a gap between what they provide out of the box and what AI in production environments actually requires.
Production systems are complex, distributed, and constantly evolving. They generate large volumes of telemetry, require domain-specific reasoning, and demand reliable performance across long, multi-step operational workflows.
Resolve AI Labs exist to close that gap by building the domain-specific models, post-training systems, evaluation infrastructure, and agentic architectures required for AI to operate effectively in production environments.
What is the mission of Resolve AI Labs?
Our mission is to enable AI systems that safely and reliably operate the worlds production software, freeing engineers to innovate more.
Will the labs focus on AI agents?
Yes, but not only agents. Resolve AI Labs will focus on both models and agents.
Foundation models alone are not enough for production operations. They need to be adapted for the domain, evaluated against real workflows, and paired with agentic systems that can take action across complex environments.
The goal of Resolve AI Labs is to advance the full stack required for AI to operate production systems effectively: domain-specific models, post-training, agentic orchestration, evaluation systems, and the operational boundaries needed to support increasing autonomy.
What is the longer-term vision for AI in production operations?
The vision is to abstract the complexity of running production systems by having AI take on most operational work, while humans shift more of their time toward building and improving the systems they run and defining how those systems should behave.
The path to get there is a progression from human-in-the-loop to human-on-the-loop to human-out-of-the-loop operations. In the near term, AI helps investigate incidents, reason across logs, metrics, traces, topology, and change data, and recommend actions with humans directly involved in execution. As capability and trust improve, AI systems move into human-on-the-loop operation, where agents can investigate, diagnose, and execute remediation within defined guardrails while humans supervise policy, exceptions, and escalation. The long-term end state is human-out-of-the-loop operation for the majority of production work, where agents take primary responsibility for investigation, diagnosis, and action, and humans step in only for exceptional or novel cases.
The goal is not better copilots. It is production environments increasingly run by AI with greater speed, consistency, and rigor.
What kind of work will Resolve AI Labs do?
Resolve AI Labs will focus on the technical foundations required for AI to manage production systems effectively, including:
- domain-specific model building and post-training for production operations
- AI reasoning across operational telemetry, including logs, metrics, traces, infrastructure events, and change history
- evaluation frameworks for measuring reliability and accuracy in real operational workflows
- synthetic data generation and simulated environments for scalable evaluation, training, and model improvement
- system architectures for scalable operational AI
- governance and guardrails for AI operating in production environments
How will the labs work with enterprises?
Resolve AI Labs will carry out this work in close collaboration with leading enterprises operating some of the most complex and business-critical production environments in the world. These environments generate a wide range of signals, evolve constantly, and require domain-specific reasoning and operational accuracy that off-the-shelf models cannot reliably provide.
Resolve AI will use these real-world challenges to shape the models, post-training methods, and evaluation systems needed for AI to perform effectively at scale in production.
What does Dhruv’s hire represent?
Dhruv is a strategic hire for Resolve AI.
Before joining the company, he led post-training efforts for large-scale foundation models at Meta Superintelligence Labs. He is joining Resolve AI to apply that experience to a different but equally demanding problem: building the domain-specific models, evaluation systems, and agentic architectures required for AI to operate reliably in production environments.
His role is to help close the gap between frontier model capability and the reliability required for real production operations.
How will this benefit engineering teams?
The work at Resolve AI Labs is focused on changing what engineers are responsible for, not just making existing workflows faster. For engineering teams, that means:
- AI systems that can investigate incidents and surface root cause without waiting for a human to step in
- Deeper, more accurate investigations across logs, metrics, traces, and topology than any one engineer can run manually
- Less time spent on operational toil across triaging, debugging, and reliability workflows
- Agents that scale across complex, multi-service environments where no single person has a complete mental model
Over time, this shifts engineers from being in every loop to setting policy, handling true exceptions, and building forward.
How will this research influence the Resolve AI platform?
Innovation from Resolve AI Labs will directly inform the development of the Resolve AI platform.
This includes improvements in:
- domain-specific reasoning across production telemetry
- post-training and adaptation for operational workflows
- evaluation of reliability and accuracy in real production tasks
- model and agent architectures for more scalable AI-driven operations
- governance and operational controls for increasingly autonomous systems
Who will collaborate with Resolve AI Labs?
Resolve AI Labs will collaborate with:
- enterprise engineering teams
- engineering leaders
- academic researchers working on applied AI systems
The goal is to ground the labs’ work in real production environments and real operational workflows.
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