Overview
If you've built AI agents, you know how challenging it is to test them. How do you even begin formulating a test plan for a technology whose input space is infinite?
Most teams fall back on a small 'golden' dataset, maybe 50 to 100 hand-picked examples. It takes ages to put together, and even then, it only covers the happy paths, missing the messy reality of real users. That's how you end up with an agent that's perfect in development, but starts hallucinating, going off-topic, or breaking policies as soon as it meets real-world scenarios.
Snowglobe fixes this problem by creating a high fidelity simulation engine for conversational AI agents! Snowglobe creates realistic personas that interact with your AI agent across diverse simulated scenarios before you go to production.
Our customers are already generating tens of thousands of simulated conversations with Snowglobe before they go into production, allowing them to speed up what would take weeks of manual scenario handcrafting and catch potential issues before they happen in prod.
Highlights
- 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.
- 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.
- 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.
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
Pricing
Dimension | Description | Cost/unit |
|---|---|---|
Generated Messages | Number of simulated messages generated by Snowglobe | $0.25 |
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We do not support refunds.
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