Snowglobe

Guardrails AI Snowglobe is a chatbot simulation and testing platform that deploys synthetic user personas to run large volumes of conversations against conversational AI agents. It is designed to surface failures, edge cases, and AI risks that manual testing typically misses, and to generate labeled datasets for evaluation and fine-tuning purposes. How it works: - Users connect their conversational AI agent via API or SDK - Snowglobe deploys realistic synthetic user personas across varied intents, tones, goals, and adversarial tactics - Hundreds of simulated conversations can be run in minutes - Results are analyzed and labeled by a judge model Core use cases: - Eval Sets for Chatbots: Generates judge-labeled test datasets from simulated conversations, covering multi-turn flows, intents, and personas, exportable to external evaluation tools - Fine-tuning Datasets: Produces training data including judge labels, preference pairs for DPO or reward models, and critique-and-revise triples for SFT, exported as JSONL - QA at Release Speed: Runs regression suites of realistic conversations per build to catch issues before production, with error rate tracking AI risks tested include hallucination, toxicity, and other failure modes. The platform has been used in contexts including airport operations, legal risk assessment, online education, and government AI verification programs.