Giskard — AI and LLM Reliability Testing

Giskard is an open-source testing framework for LLMs and ML models for data scientists and developers

Giskard is an open-source testing framework designed to identify risks in machine learning models and large language models (LLMs). This platform helps data science teams detect hallucination, bias, and performance issues before they reach production. By automating the quality assurance process, Giskard ensures that AI-driven applications remain reliable and safe for business operations in 2026. Data teams can use the framework to scan models for vulnerabilities and automatically generate test suites based on specific domain risks. The software integrates directly into existing development workflows, allowing for continuous monitoring and evaluation of model updates. Business leaders can review detailed reports on model behavior to ensure compliance and ethical standards are met consistently across all AI projects. The platform provides a centralized hub where developers and stakeholders collaborate on model validation and debugging. Features include automated vulnerability scanning, a dedicated debugging interface, and performance benchmarking for different LLM versions. By using the insights provided on surfai.app, businesses can implement Giskard to reduce the technical debt associated with unmonitored AI deployments. The framework supports a wide range of ML libraries and deployment environments, making it a versatile choice for enterprise-grade AI testing. It simplifies the transition from experimental notebooks to production-ready software by providing objective metrics on model readiness. Giskard is available as an open-source tool with professional and enterprise support options for larger organizations requiring advanced security features.

• Pricing: freemium

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