Snowglobe

Snowglobe is a simulation environment designed for teams working with large language models (LLMs) to test their applications against realistic user behavior. It enables users to simulate diverse, realistic scenarios by deploying synthetic personas that can run hundreds of conversations in minutes. This approach helps uncover failures often missed by manual testing while providing judge-labeled datasets for evaluation and fine-tuning.

The platform addresses the limitations of manual chatbot testing, which is often slow, labor-intensive, and fails to cover edge cases. By simulating user interactions at scale, Snowglobe generates conversation data that spans varied intents, personas, tones, goals, and even adversarial tactics. This ensures comprehensive testing and facilitates the creation of high-quality datasets.

Snowglobe supports multiple use cases, including generating judge-labeled evaluation datasets that reflect real-world behavior, creating fine-tuning datasets with high-signal training data, and enabling rapid QA testing to identify and address issues before deployment. Its automated simulation capabilities allow teams to save time, improve coverage, and confidently refine their models to deliver better performance in production.