Open datasets and evals for agents that act.

New: CartTrace-100k, our first release: 101,416 journeys of an AI shopping agent through checkout and payment, across 1,044 stand-in stores in 25 categories. Read the research note or the paper.

General Machines is an applied AI research lab. We build open datasets, evaluations and small models for frontier AI labs and applied teams, and we publish the research behind them. Our work is aimed at two domains: the agentic web, where we measure and train AI agents that act across the internet, and physical AI, where we start with retail: shelf data and small vision models for stores.

Collecting high-quality behavioral data at scale is expensive, unglamorous, and undervalued. We think it is also the most important bottleneck in AI progress. Frontier labs can train better models if they have better data. Applied teams can ship better agents if they can measure agent behavior precisely. We exist to help close both gaps.

Our first applied arm is Machine Commerce, which covers everything in agentic commerce: products and services that help merchants get ready for AI agents that shop, compare and pay on behalf of people. Our research on how those agents behave feeds its work, and its work tells us what to measure next.

Next is Commerce-1, a small model that predicts when a shopping agent is about to fail. To work with us, write to founders@generalmachines.ai.