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Platform for building RL environments and evals
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 (YC W25) is developing agentic evals and RL environments for Computer Use Agents (CUAs) that browse the web for frontier AI labs. Our CUA Evals framework is the first comprehensive evaluation tool for CUAs.
People don't actually know if AI agents are working reliably. To make AI agents work in the real world, we need detailed evals for a huge range of tasks.
We're backed by Y Combinator, and work closely with frontier AI labs to provide agent evaluation and training infrastructure at scale.
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.
Key people at hud.
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.
hud has raised $500K across 1 funding round. Most recently, it raised $500K Seed in December 2024.
| Date | Round | Lead Investors | Other Investors |
|---|---|---|---|
| Dec 1, 2024 | $500K Seed | Y Combinator |
