AI startup Deeptune has raised a $43 million Series A to build what it calls “training gyms” for AI agents, Fortune has learned exclusively. Andreessen Horowitz led the round, joined by 776, Abstract Ventures, and Inspired Capital, with angels including OpenAI researcher Noam Brown, Mercor CEO Brendan Foody, and Applied Compute CEO Yash Patil.
Deeptune creates high‑fidelity reinforcement learning (RL) environments that simulate the day‑to‑day workflows of roles like accountants, customer support reps, and DevOps engineers, so AI agents can learn to navigate multi‑step tasks across popular workplace software such as Slack, Salesforce, and other ticketing, finance, and monitoring tools. “We essentially build simulations of digital work that look like the workspace of an accountant or a lawyer or a software engineer,” cofounder and CEO Tim Lupo told Fortune.
Lupo likens today’s models to pilots who have “only ever read books or watched tutorials.” “You wouldn’t have a pilot who has only ever read books or watched tutorials fly a plane. You would put them in a flight simulator,” he said. “What we build are essentially the flight simulators for AI doing work across the economy.”
Deeptune’s bet reflects a broader shift in AI from training on static web‑scale data to running large‑scale reinforcement learning in synthetic and interactive environments—a direction seen in recent agentic RL work on tool‑use agents at Microsoft, and OpenAI’s computer‑using agent. The global reinforcement learning market, including tools and environments, is projected to grow from roughly $11.6 billion in 2025 to more than $90 billion by 2034, according to ResearchAndMarkets.
