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The testing
platform for
AI systems

Protect your company against biases, performance & security issues in AI models.
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Open-Source
Listed by Gartner
AI Trust, Risk and Security
# Get started
pip install giskard[llm]
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Trusted by Enterprise AI teams
Why?

AI pipelines are broken

AI risks, including quality, security & compliance, are not properly addressed by current MLOps tools.
AI teams spend weeks manually creating test cases, writing compliance reports, and enduring endless review meetings.
AI quality, security & compliance practices are siloed and inconsistent across projects & teams,
Non-compliance to the EU AI Act can cost your company up to 3% of global revenue.

Enter Giskard:
AI Testing at scale

Automatically detect performance, bias & security issues in AI systems.
Stop wasting time on manual testing and writing custom evaluation reports.
Unify AI Testing practices: use standard methodologies for optimal model deployment.
Ensure compliance with the EU AI Act, eliminating risks of fines of 3% of your global revenue.

“Giskard really speeds up input gatherings and collaboration between data scientists and business stakeholders!”

Head of Data
Emeric Trossat

Giskard has streamlined our entire testing process thanks to their solution that makes AI model testing truly effortless.

Corentin Vasseur
ML Engineer & Responsible AI Manager
Corentin Vasseur

Giskard Vision has become our go-to tool for testing our landmark detection models. It allows us to identify biases in each model and make informed decisions.

Alexandre Bouchez
Senior ML Engineer
Alexandre Bouchez
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TRY IT in COLAB
Giskard Open-Source

Easy to integrate for data scientists

In a few lines of code, identify vulnerabilities that may affect the performance, fairness & security of your LLM. 

Directly in your Python notebook or Integrated Development Environment (IDE).

import giskard‍
qa_chain = RetrievalQA.from_llm(...)
model = giskard.Model(
   qa_chain,
   model_type="text_generation",
    name="My QA bot",
    description="An AI assistant that...",
   feature_names=["question"],
)
giskard.scan(model)
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Giskard Enterprise

Collaborative AI Quality, Security & Compliance

Entreprise platform to automate testing & compliance across your GenAI projects.
Try our latest open-source release!

Evaluate RAG Agents automatically

Leverage RAGET's automated testing capabilities to generate realistic test sets, and evaluate answer accuracy for your RAG agents.
TRY RAGET

Who is it for?

AI Engineers
Heads of AI teams
AI Governance officers
You work on business-critical AI applications.
You work on enterprise AI deployments.
You spend a lot of time to evaluate AI systems.
You’re preparing your company for compliance with the EU AI Act and other AI regulations.
You have high standards of performance, security & safety in AI systems.

Join the community

Welcome to an inclusive community focused on AI Quality, Security & Compliance! Join us to share best practices, create new tests, and shape the future of AI standards together.

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All those interested in AI Quality, Security & Compliance are welcome!

All resources

Knowledge articles, tutorials and latest news on AI Quality, Security & Compliance

See all
Giskard integrates with LiteLLM to simplify LLM agent testing

[Release notes] Giskard integrates with LiteLLM: Simplifying LLM agent testing across foundation models

Giskard's integration with LiteLLM enables developers to test their LLM agents across multiple foundation models. The integration enhances Giskard's core features - LLM Scan for vulnerability assessment and RAGET for RAG evaluation - by allowing them to work with any supported LLM provider: whether you're using major cloud providers like OpenAI and Anthropic, local deployments through Ollama, or open-source models like Mistral.

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EU's AI liability directives

AI Liability in the EU: Business guide to Product (PLD) and AI Liability Directives (AILD)

The EU is establishing an AI liability framework through two key regulations: the Product Liability Directive (PLD), taking effect in 2024, and the proposed AI Liability Directive (AILD). The PLD introduces strict liability for defective AI systems and software, while the AILD addresses negligent use, though its final form remains under debate. Learn in this article the key points of these regulations and how they will impact businesses.

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Giskard-vision: Evaluate Computer Vision tasks

Giskard Vision: Enhance Computer Vision models for image classification, object an landmark detection

Giskard Vision is a new module in our open-source library designed to assess and improve computer vision models. It offers automated detection of performance issues, biases, and ethical concerns in image classification, object detection, and landmark detection tasks. The article provides a step-by-step guide on how to integrate Giskard Vision into existing workflows, enabling data scientists to enhance the reliability and fairness of their computer vision systems.

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Automate your testing and compliance