Foundry

We’re building the an end-to-end web simulator for browser agent training and evals. Our system enables teams to test, benchmark, and optimize browser automation models at scale. - Deterministic Web Simulation → Stable, reproducible testing with versioned web snapshots. - Live Web Evaluation → Identify failures caused by UI drift, captchas, and dynamic content. - Automated Annotation & Labeling → Generate high-quality training data for benchmarking. - RL-Driven Agent Optimization → Improve models with scalable, feedback-driven learning. By combining synthetic user simulations, automated evaluations, and large-scale benchmarking, we help teams build more reliable web agents that handle real-world environments with confidence.