Deploy realistic personas to run hundreds of conversations in minutes, reveal failures manual testing misses, and generate judge-labeled datasets for evals and fine-tuning.
Good synthetic data is hard to generate, with the chief reason being that it's hard to create diversity of content. [...] When we started using Snowglobe, the clearest difference we saw was how realistic the synthetic user personas felt compared to any synthetic data that we'd seen before. We have completely switched to using SnowGlobe for this data.
Aman Gupta
Head of AI, Masterclass
Stop hand-building chatbot scenarios
Manual chatbot testing misses failures that break in production. Simulation generates the conversation data you need in minutes and surfaces those issues early, with judge-labeled datasets for evals and fine-tuning.
Manual testing is slow and shallow
Writing conversations one by one limits coverage to what humans think of. Weeks of work, still missing edge cases.
Simulate realistic users at scale
Run hundreds of conversations in minutes across varied intents, personas, tones, goals, and adversarial tactics.
Use Cases Powered by Simulation
Simulated user conversations you can test with and train on.
Eval Sets for Chatbots
Generate judge-labeled test datasets from simulated user conversations in minutes. Cover real behavior across intents, personas, tones, and multi-turn flows. Export to your eval tools.
Fine-tuning Datasets
Generate high-signal training data from the same runs: judge labels, preference pairs for DPO or reward models, and critique-and-revise triples for SFT. Export clean JSONL ready for training.
QA at Release Speed
Run hundreds of realistic conversations per build to catch issues manual testing misses. Save suites for regression and track error rates so problems don’t reach production.