G

The testing
platform for AI models

Protect your company against biases, performance & security issues in AI models.

From tabular models to LLMs
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Try it in Colab
Listed by Gartner
AI Trust, Risk and Security
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pip install giskard[llm]
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Giskard - Open-source testing framework for LLMs & ML models | Product Hunt

Trusted by leading 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 models.
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.

Mitigate AI Risks with our holistic platform
for AI Quality, Security & Compliance

Giskard Library

Open-Source
Open-source Python library to identify & control risks in ML models and LLMs automatically, with a complete test coverage of performance, ethics & security metrics.

Giskard Hub

Enterprise Hub for teams to collaborate on top of the open-source library, with compliance dashboards, debugging, human feedback, explainability and secure access controls.
Get started
TRY IT in COLAB
Giskard Library

Open-source & easy to integrate

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

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 Hub

Collaborative AI Quality, Security & Compliance

Entreprise platform to test, debug & explain your AI models collaboratively.
Try our latest 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?

Data scientists
ML Engineers
AI Governance officers
You work on business-critical AI applications.
You spend a lot of time to evaluate AI models.
You want to work with the best Open-source tools.
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 models.

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

Head of Data
Emeric Trossat

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

Head of Data
Emeric Trossat

"Giskard has become a strong partner in our purpose for ethical AI. It delivers the right tools for releasing fair and trustworthy models."

Head of Data Science
Arnault Gombert

"Giskard enables to integrate Altaroad business experts' knowledge into our ML models and test them."

Jean MILPIED

"Giskard allows us to easily identify biases in our models and gives us actionable ways to deliver robust models to our customers."

Chief Science Officer
Maximilien Baudry

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

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Why LLM evaluation is important

Guide to LLM evaluation and its critical impact for businesses

As businesses increasingly integrate LLMs into several applications, ensuring the reliability of AI systems is key. LLMs can generate biased, inaccurate, or even harmful outputs if not properly evaluated. This article explains the importance of LLM evaluation, and how to do it (methods and tools). It also present Giskard's comprehensive solutions for evaluating LLMs, combining automated testing, customizable test cases, and human-in-the-loop.

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Red Teaming LLM Applications course

New course with DeepLearningAI: Red Teaming LLM Applications

Our new course in collaboration with DeepLearningAI team provides training on red teaming techniques for Large Language Model (LLM) and chatbot applications. Through hands-on attacks using prompt injections, you'll learn how to identify vulnerabilities and security failures in LLM systems.

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Giskard's LLM Red Teaming

LLM Red Teaming: Detect safety & security breaches in your LLM apps

Introducing our LLM Red Teaming service, designed to enhance the safety and security of your LLM applications. Discover how our team of ML Researchers uses red teaming techniques to identify and address LLM vulnerabilities. Our new service focuses on mitigating risks like misinformation and data leaks by developing comprehensive threat models.

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