We’re building the first end-to-end evaluation and training platform for web agents. 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.