Giskard provides an automated testing platform for AI models that identifies performance, bias, and security issues, ensuring compliance with the EU AI Act. By streamlining the testing process, Giskard reduces the time spent on manual evaluations and enhances the consistency of AI quality and security practices across enterprise deployments.
Funding
$40K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
GAFounders
Product
Problem
AI models often suffer from performance issues, biases, and security vulnerabilities that are not adequately addressed by existing MLOps tools. This leads to manual testing processes, inconsistent quality control, and potential non-compliance with regulations like the EU AI Act. The lack of standardized testing methodologies increases the risk of deploying unreliable and potentially harmful AI systems.
Solution
Giskard offers an automated AI testing platform designed to identify and mitigate performance, bias, and security issues in AI models. The platform streamlines the testing process, reducing the reliance on manual evaluations and promoting consistent AI quality and security practices across enterprise deployments. By automating the detection of vulnerabilities and ensuring compliance with AI regulations, Giskard helps organizations deploy reliable and trustworthy AI systems at scale. The platform facilitates collaboration between data scientists and business stakeholders, ensuring comprehensive AI governance.
Target Audience
Giskard is primarily aimed at AI engineers, heads of AI teams, and AI governance officers working on business-critical AI applications and enterprise AI deployments, particularly those preparing for compliance with the EU AI Act and other AI regulations.
Features
- Automated detection of performance degradation, biases, and security vulnerabilities in AI models
- Comprehensive test suite for evaluating model robustness, fairness, and compliance
- Collaborative platform for data scientists, AI engineers, and governance officers to streamline testing workflows
- Integration with popular machine learning frameworks and development environments via a Python SDK
- Support for evaluating RAG agents with automated test generation and accuracy assessment
- Customizable dashboards and reporting tools for monitoring AI quality metrics and compliance status
- Open-source core with enterprise features for collaborative AI quality, security, and compliance
- Integration with NVIDIA NeMo Guardrails for enhanced safety and reliability of LLM-based applications