How It Works
Snowglobe generates AI personas that create test scenarios for user applications, then iteratively focuses on the highest-risk personas based on test results.Getting Started
Get started with Snowglobe in under 5 minutes.
See Examples
See examples Snowglobe in action for various kinds of chatbots.
Core Concepts
- AI chatbot: The AI chatbot that Snowglobe will stress-test. In order to stress-test a chatbot, you need to provide a connection to the chatbot (e.g. a URL to an API endpoint or start a local server) as well as a short description of the chatbot’s purpose and behavior.
- Simulations: One run of Snowglobe is called a simulation. During a simulation, Snowglobe will generate a set of personas that will interact with the chatbot’s endpoints to create simulated conversations.
- Personas: Synthetic user that Snowglobe creates during a simulation. Personas have their own goals, personality, and behavior, and they maintain their own state as they interact with the chatbot’s endpoints.
- Scenarios: Each simulated conversation is called a scenario. Snowglobe will generate scenarios at scale during simulation.
- Metrics: Judgements on the chatbot’s performance on each scenario. Users select the metrics that they want to track during simulation.
Why Snowglobe?
- Testing at scale - Simulate thousands of user interactions that manual testing cannot practically cover.
- Rich persona modeling - Multi-dimensional personas generate realistic, diverse interactions across varied user types and behavioral patterns.
- High-quality test data - Automated generation produces comprehensive scenarios with authentic conversation flows and edge cases.
- Catches issues before deployment - Identify vulnerabilities and failure modes during development phases before they reach production.
- Workflow integration - Integrates into CI/CD pipelines. Data can be exported to CSV, JSON, or exported to your favorite eval tool.