Data from real-world, digital, and simulated interactions for agent training and evaluation.
Sieve collects multimodal data from real-world, digital, and simulated environments, including interaction traces. Its process includes filtering data for task quality and other criteria, indexing traces, and adding annotations such as action metadata and UI events. The company says it packages training-ready datasets, evaluation sets, and environments for secure delivery. Teams can request samples, define dataset requirements such as metadata and licensing, and receive pre-packaged or custom deliveries. The official site presents interaction data as part of a broader multimodal data offering, rather than as a standalone product.
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