Simulate real users to test your AI before launch
Snowglobe is a simulation environment for LLM teams to test how their applications respond to real-world user behavior. Run full workflows through realistic scenarios, catch edge cases early, and confidently improve before deploying to production.
Snowglobe generates high-signal training data from simulated conversations, including judge labels, preference pairs for DPO, and critique-and-revise triples for SFT. Users export clean JSONL ready for training, eliminating manual dataset curation.
Snowglobe addresses the challenge of creating diverse synthetic data by generating realistic user personas and multi-turn conversation flows. Users report that Snowglobe personas feel significantly more realistic compared to other synthetic data sources.
Snowglobe simulates hundreds of conversations to surface previously overlooked failure patterns and edge cases. Comprehensive reports reveal performance across user types and interaction styles, helping teams catch unreliable agent behavior early.
Snowglobe deploys realistic personas to run hundreds of conversations in minutes, revealing failures that manual testing misses. This enables teams to achieve high-coverage testing across varied intents, personas, tones, and adversarial tactics without weeks of manual scenario writing.
Snowglobe enables teams to run hundreds of realistic conversations per build, save test suites for regression, and track error rates. This catches issues before they reach production, dramatically reducing post-deployment failures.