Galtea provides an AI evaluation platform that automatically generates realistic user queries, edge cases, and synthetic personas to create extensive test suites without manual effort. It runs AI agents against built‑in and custom accuracy, security, safety, and behavioral metrics, surfacing regressions early in the development pipeline and reducing manual testing time by hundreds of hours.
Funding
Funding not disclosed
Founders
Product
Problem
AI developers often lack scalable methods to generate comprehensive test cases and evaluate model changes, leading to missed edge cases, security vulnerabilities, and regressions that only surface after deployment.
Solution
Galtea offers an AI evaluation platform that automatically creates realistic user queries, adversarial inputs, and synthetic personas from system prompts, eliminating the need for manual test authoring or external datasets. The generated test suites can scale to thousands of scenarios and are evaluated against built-in or custom accuracy, security, safety, and behavioral metrics. Metrics are calibrated with human labels and can be extended using code, LLM-as-a-judge, or human evaluation queues, with automatic suggestions based on product specifications. Results are presented with observability tools that highlight regressions, enabling proactive blocking of faulty releases before they reach users. The platform integrates with CI/CD pipelines and supports pre‑production and post‑deployment monitoring, reducing operational costs for AI validation.
Target Audience
Primary customers are AI product teams, LLM developers, and enterprises that need to validate and monitor AI agents and language models at scale, from solo developers to large organizations.
Features
- Automatic generation of realistic queries, edge cases, adversarial inputs, and synthetic personas without requiring a dataset
- Pre‑built accuracy, security, safety, and behavioral metrics calibrated against human labels
- Custom metric creation in under five minutes via code, LLM-as-a-judge, or human evaluation queues
- CI/CD integration for continuous regression testing and automated quality gates
- Proactive monitoring that simulates user interactions upstream of deployment to catch failures early
- Credit‑based usage model with granular costing for different operation complexities