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§ Private Profile · San Francisco, CA, USA
Platform for building reinforcement learning environments and evaluations for AI agents, focused on computer use agents.
hud has raised $500K across 1 funding round.
Key people at hud.
hud was founded in 2025 by Jay Ram (Founder) and Parth Patel (Founder) and Lorenss Martinsons (Founder).
hud has raised $500K in total across 1 funding round.
HUD, a San Francisco, CA-based platform, provides tools for building reinforcement learning (RL) environments and evaluations for AI agents, particularly those designed for computer use and web browsing. The platform enables users to define tools, scenarios, and rewards in isolated sandboxes to test agent performance, generate training traces, and train specialized models. It supports scalable evaluations, RL training, and a model gateway for multiple large language models, operating on a usage-based pricing model starting at $0.25 per environment hour. The company, part of the Y Combinator W25 batch, currently employs 15 individuals and works with frontier AI labs, including a case study with Sentry that demonstrated 2x model improvement. YC partner Aaron Epstein is associated with the company. HUD was founded in 2025 by Jay Ram, Lorenss Martinsons, and Parth Patel.
hud has raised $500K across 1 funding round. Most recently, it raised $500K Seed in December 2024.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Dec 1, 2024 | $500K Seed | — | Y Combinator | Announced |
Key people at hud.
hud was founded in 2025 by Jay Ram (Founder) and Parth Patel (Founder) and Lorenss Martinsons (Founder).
hud has raised $500K in total across 1 funding round.
hud's investors include Y Combinator.
HUD is a platform focused on building reinforcement learning (RL) environments and agentic evaluation tools specifically for AI agents known as Computer Use Agents (CUAs) that interact with software and browse the web autonomously. It provides a comprehensive framework for evaluating and training AI agents across hundreds of tasks and environments, enabling researchers and developers to reliably measure agent performance and improve their capabilities at scale. HUD serves frontier AI labs and researchers by offering infrastructure for agent evaluation, training, and environment creation, facilitating faster iteration and deployment of AI agents in real-world applications[1][3].
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HUD was founded in 2025 and is part of Y Combinator’s Winter 2025 batch. The founding team, based in San Francisco, includes key partners such as Aaron Epstein. The idea emerged from the need to create a standardized, scalable way to evaluate and train AI agents that autonomously interact with software and the web, addressing a critical gap in AI development where agent reliability was poorly understood. Early traction includes collaboration with frontier AI labs and rapid adoption by researchers who require detailed, real-time evaluation metrics for their agents[1][3].
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HUD rides the wave of increasing demand for trustworthy, autonomous AI agents capable of performing complex tasks in real-world software environments. As AI systems move beyond static models to interactive agents, the need for rigorous evaluation and training infrastructure becomes critical. Market forces such as the rise of large language models, autonomous software agents, and reinforcement learning applications create a fertile environment for HUD’s platform. By enabling scalable, reproducible agent evaluation, HUD influences the broader AI ecosystem by setting standards for agent reliability and accelerating deployment readiness[1][3][5].
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Looking ahead, HUD is positioned to become a foundational infrastructure provider for AI agent development. As autonomous agents proliferate in industries like web automation, customer service, and software testing, HUD’s platform will likely expand its benchmarks, environment diversity, and enterprise offerings. Trends such as multi-agent systems, improved RL algorithms, and integration with large language models will shape HUD’s evolution. Its influence may grow from a research tool to a critical component in commercial AI agent deployment pipelines, helping ensure AI agents are safe, reliable, and effective in complex real-world settings[1][3].
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This synthesis highlights HUD’s unique role as a cutting-edge platform enabling the next generation of AI agents through scalable evaluation and training infrastructure, backed by a strong founding team and Y Combinator support.
