Giskard

Giskard is a comprehensive testing platform dedicated to ensuring the quality, security, and reliability of AI agents, particularly those built on Large Language Models (LLMs). In a landscape where over 90% of GenAI projects fail to reach production due to hidden risks, Giskard provides the necessary tools for enterprise AI teams, data scientists, and QA professionals to build and deploy AI with confidence. The platform addresses critical vulnerabilities like hallucinations, misinformation, prompt injections, data leaks, toxicity, and biases, preventing potential reputational damage and ensuring regulatory compliance.

Founded by experienced AI professionals from Dataiku and Thales, Giskard's mission is to make AI trustworthy. The platform is built on the principle of turning business knowledge into actionable AI tests, allowing even non-technical team members to participate in the validation process. It offers both an open-source Python library for individual developers and an enterprise-grade LLM Hub for teams requiring scalable, collaborative testing solutions.

How to use Giskard

Giskard streamlines the AI testing process into a few key steps. First, users connect their LLM application and business data to the platform. Giskard then automatically generates exhaustive test suites tailored to the specific industry and use case. These tests systematically scan for a wide range of vulnerabilities. The platform facilitates a continuous testing loop, integrating with CI/CD pipelines to monitor key performance metrics and alert teams to new threats. For a deeper analysis, teams can use the collaborative dashboard to annotate results, debug issues, and refine the AI's behavior, ensuring that business-specific requirements are met. The open-source library allows developers to implement these tests directly within their Python code, making it ideal for early-stage projects and individual data scientists.

Core Features of Giskard

• Exhaustive Risk Detection: Identifies a wide range of issues including hallucinations, prompt injections, data disclosure, toxicity, stereotypes, and robustness failures.
• Automated Test Generation: Connects to your business data to automatically create comprehensive test scenarios, including tests for Retrieval-Augmented Generation (RAG) quality and function/tool calling.
• Continuous Red Teaming: Proactively and continuously tests AI agents against emerging threats to ensure ongoing protection after deployment.
• Collaborative Dashboard: An intuitive interface for product, QA, and technical teams to work together on annotating, debugging, and validating AI outputs.
• Enterprise-Grade Security & Deployment: Offers flexible deployment options (SaaS, on-premise, private cloud) with robust security features like role-based access control (RBAC), SSO integration, and GDPR compliance.
• Open-Source Python Library: A free, powerful library for AI engineers and data scientists to integrate AI testing directly into their development workflow.
• Independent Validation: Provides quantitative metrics and third-party expert validation to build trust with stakeholders.

Use Cases for Giskard

Giskard is versatile and can be applied across various industries and applications. For example, in customer service, it can be used to test AI chatbots to ensure they provide accurate information and do not hallucinate or leak sensitive customer data. In finance and insurance, it helps validate models for fraud detection and ensure they are free from biases. Giskard is also a leading tool for benchmarking RAG systems, comparing different models and approaches to find the optimal solution for applications that rely on external knowledge bases. Companies like L'Oréal have used Giskard to evaluate and enhance advanced AI models for tasks like Facial Landmark Detection, improving accuracy and reliability.

Advantages of Giskard

The primary advantage of Giskard is its ability to de-risk AI projects, significantly increasing their chances of successful deployment. It bridges the gap between technical development and business requirements by providing a common platform for collaboration. This collaborative approach ensures that the AI's behavior aligns with business logic and ethical standards. The platform's automation capabilities save significant time and resources in the testing phase, while its continuous monitoring provides peace of mind post-deployment. With both a powerful open-source offering and a secure, scalable enterprise solution, Giskard caters to the entire spectrum of AI development needs, from individual experimentation to large-scale, mission-critical deployments.

Pricing and Plans

Giskard offers a freemium pricing model with two main tiers:

• Open-Source: This plan is completely free and ideal for solo data scientists, AI engineers, and early-stage projects. It includes a Python library for testing AI agents in code, exhaustive security vulnerability detection, and automated generation of RAG quality tests. Support is provided through a public Discord community.
• Enterprise: This is a paid annual subscription priced per LLM agent, designed for enterprise AI teams that need testing at scale. It includes all open-source features plus a collaborative dashboard, continuous red-teaming with alerts, advanced security (on-premise, private cloud, or SaaS deployment), role-based access control, SSO, and a secure API for CI/CD automation. It also comes with dedicated support and priority SLAs. A quote can be requested directly from the Giskard team.