Snowglobe is the high-fidelity simulation environment for LLM teams to test how their conversational AI agents respond to real-world user behavior—before deployment. Instead of relying on tiny, hand-picked “golden” datasets, Snowglobe generates realistic personas and runs them through thousands of full, multi-turn conversations, revealing edge cases and failure modes that manual testing misses.
Built by engineers with experience in high-stakes simulation (including self-driving car programs at Apple), Snowglobe brings the simulation-first mindset to AI agents. Rich persona modeling ensures diversity and realism, grounding scenarios in your agent’s context. You can test both normal-user behaviors and adversarial edge cases, then export datasets directly to Hugging Face or your favorite evaluation and tracing tools.
Perfect for teams building chatbots, voice assistants, or AI agents, Snowglobe accelerates QA, pentesting, and fine-tuning. In minutes, you can generate judge-labeled eval sets, high-signal fine-tuning data, and comprehensive performance reports—allowing you to ship faster, with confidence that your AI can handle the messy reality of real users.
