Markov

Markov occupies a narrow but critical niche: training data for computer-use AI. If your team is building an agent that clicks buttons, fills forms, or navigates software, their dataset is likely the best available. The focus on human-generated demonstrations (not synthetic) adds realism that RL environments often lack. However, this is not a plug-and-play model store—you need in-house ML expertise to leverage the data effectively. Compared to general-purpose datasets like Common Crawl or synthetically generated interaction logs, Markov offers higher fidelity at a higher cost and smaller scale. Where it falls short: no pre-trained models, no inference API, and pricing that's opaque—you'll need to negotiate. We'd recommend Markov for R&D labs with dedicated agent-training pipelines; for smaller teams, the investment may be too heavy without proven ROI. The company's messaging around 'world's most advanced' is bold, but in a field this nascent, it's plausible. Watch for synthetic data alternatives from big labs that might commoditize this space.