Enterprise Account Executive

Resolve AI has expanded its platform with always-on background agents that continuously perform operational work in production environments. The San Francisco-based company introduced a new investigation architecture delivering over two times improvement in root cause accuracy and collaborative workflows where engineers and AI agents work from shared evidence. The platform's background agents handle ongoing tasks including monitoring deployments, investigating alerts, auditing operational hygiene, tracking configuration changes and surfacing cost anomalies. Agents typically triage on-call alerts within five minutes before engineer involvement. The system uses specialised agents to investigate multiple hypotheses in parallel whilst independently verifying conclusions. Resolve AI counts Coinbase, DoorDash, MSCI, Salesforce and Zscaler amongst its customers. The platform is now accessible via REST API and MCP server for integration into broader infrastructure workflows.

Resolve AI announces Series A Extension at a $1.5B valuation and launches Resolve AI Labs to advance AI systems for complex production environments. Resolve AI, the AI for running and operating software in production, 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: Apr 16, 2026 Prev Next 1 of 42,809 * 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.

Resolve AI has raised $40 million in a Series A extension led by DST Global and Salesforce Ventures, valuing the company at $1.5 billion. The startup, which emerged from stealth 18 months ago, has now raised over $190 million and serves enterprise customers including Coinbase, DoorDash, Salesforce and Zscaler. The company has launched Resolve AI Labs to develop domain-specific models and agentic systems for managing complex production environments. The lab will be led by Dhruv Mahajan, former head of post-training for Meta's Llama foundation models, as chief AI scientist. Resolve AI builds AI systems for operating software in production, focusing on reasoning across operational telemetry and managing multi-step workflows. The new funding will support platform development, market expansion and research initiatives.