Resolve AI, a startup focused on keeping enterprise software running once it has been built and deployed, has raised $40 million in a Series A Extension at a $1.5 billion valuation. The round was led by DST Global and Salesforce Ventures, and brings the company’s total funding to more than $190 million. Founders Spiros Xanthos and Mayank Agarwal, both veterans of the software observability field, started the company around a straightforward but costly problem: production systems break, and fixing them takes too long.

When software fails in a live environment, engineering teams have to move fast. They pull data from logs, performance metrics, error traces, and infrastructure alerts, often across many services at once, trying to piece together what went wrong before the damage compounds. That process can take hours. The global market for AI tools that help automate this kind of work was valued at $5.3 billion in 2024 and is projected to grow at 22.4% per year through 2034, driven by the growing complexity of modern software systems.

Resolve AI counts Coinbase, DoorDash, MSCI, Salesforce, and Zscaler among its enterprise customers. The company emerged from stealth just 18 months ago.

Why general-purpose AI falls short in production environments

Modern software does not run as a single program. It runs as dozens, sometimes hundreds, of interconnected services, each producing a constant stream of data. When something breaks, the cause rarely sits in one place. It hides across multiple systems, teams, and time zones. Responding to an incident still relies heavily on human engineers making judgment calls under pressure, comparing alerts, checking logs, and tracing dependencies. The process is slow and hard to scale.

Off-the-shelf AI models have not changed this much. They were not trained on the kind of data that production environments generate, such as telemetry logs, infrastructure events, and system topology. They do not understand how operational dependencies work, and they cannot reason reliably across the noisy, fragmented signals that engineers have to deal with every day. The gap is not just about capability. It is about the fact that these models were never designed for this domain in the first place.

“As early investors in foundation models, we’ve seen firsthand how AI is reshaping how software gets built. 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.”

Zak Kokosa, Principal, Salesforce Ventures

How Resolve AI plans to use the new funding

The $40 million will go toward three things: developing the Resolve AI platform further, expanding its sales and go-to-market operations, and funding a new research division called Resolve AI Labs. The Labs are built on the premise that production operations need AI models trained specifically for that environment, not general models adapted with better instructions.

The research agenda covers a range of technical areas: building domain-specific models, post-training those models on operational data, developing frameworks to measure reliability in real workflows, generating synthetic data for training purposes, and establishing governance controls for AI systems that take action in live environments.

Dhruv Mahajan will lead the Labs as Chief AI Scientist. He joins from Meta Superintelligence Labs, where he led post-training work on large-scale Llama foundation models. At Resolve AI, he will apply that experience to a narrower and more demanding problem: getting AI to perform reliably inside production systems rather than in general-purpose settings.

“Production systems are noisy, incomplete, and constantly changing. 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.”

Dhruv Mahajan, Chief AI Scientist at Resolve AI

How Resolve AI’s platform works and where the company is headed

Xanthos and Agarwal both spent their careers in observability, which refers to the practice of monitoring and understanding the internal state of complex software systems. That background shaped the product. Instead of building on top of existing monitoring tools, they designed a platform from scratch to handle what production environments actually demand: reasoning across operational data, managing long-running workflows, and keeping up with systems that change constantly.

The platform investigates incidents, identifies root causes, and coordinates remediation. Salesforce describes a clear before and after:

“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.”

Meir Amiel, President, Chief Trust and Infrastructure Officer, Salesforce

The company’s longer-term goal is to have AI handle most of the operational work in production environments. The path runs through three stages. First, AI assists human engineers who remain in the loop for every decision. Then, AI starts executing within defined boundaries while humans supervise. Eventually, AI takes primary responsibility for investigating and resolving issues, with humans stepping in only when something falls outside normal parameters. That last stage is what Resolve AI is building toward.

The investors behind the round

DST Global and Salesforce Ventures led the Series A Extension. Both firms have invested in foundation model companies and have experience in enterprise software infrastructure.

“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. 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.”

Rahul Mehta, Co-founder and Managing Partner at DST Global

Origianl Creator: Ekaterina Pisareva
Original Link: https://justainews.com/companies/funding-news/resolve-ai-raises-40m-at-1-5b-valuation/
Originally Posted: Thu, 16 Apr 2026 11:29:35 +0000