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LLM Evaluation Hub

Detect all GenAI risks, test continuously, and validate AI agents against your business needs.
Evaluate now

From LLM evaluation to continuous Red Teaming

Detect

Create tests to detect hallucinations and security vulnerabilities

Annotate

Collaborate with business experts to validate test scenarios

Automate

Integrate validated tests into your deployment pipeline

Protect

Prevent new risks with continuous Red Teaming & alerts
Detect hallucinations and security issues

Exhaustive AI risk detection

Detect RAG-based hallucinations using your internal knowledge bases. Secure your AI agents through vulnerability scanning based on industry taxonomies like OWASP.

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Continuous Red Teaming with alerting system

Proactive AI risk prevention

Get alerted with quantitative KPIs proactively whenever a new risk of hallucinations or security issue arises.

We proactively generate and run new tests by connecting to live vulnerability databases, alerting you with actionable metrics.

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AI Risk Officer
AI Engineer
GenAI Lead
AI Product Owner

Collaboration across teams

Automate the creation of business-specific tests with efficient feedback loops from domain experts.

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AI Risk Officer
AI Engineer
GenAI Lead
AI Product Owner

Flexible API integration

From development environments to production pipelines, integrate AI testing wherever you need it. Our versatile API enables automated workflows to fit your enterprise needs.

from giskard_hub import HubClienthub = HubClient()

model = hub.
models.retrieve("043d00df-0bc6-4a47-9c5f-cd97ae82b06b")
dataset = hub.
datasets.retrieve("50d20242-e262-4ab8-bfd6-6fcdc1d5b181")

hub.
evaluate(
   model=model.
id,
   dataset=dataset.
id,
   name=
"eval-prompt-injections"
)

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FAQ

What is the difference between Giskard and LLM platforms like LangSmith?
  • Automated Vulnerability Detection:
    Giskard not only tests your AI, but also automatically detects critical vulnerabilities such as hallucinations and security flaws. Since test cases can be virtually endless and highly domain-specific, Giskard leverages both internal and external data sources (e.g., RAG knowledge bases) to automatically and exhaustively generate test cases.
  • Proactive Monitoring:
    At Giskard, we believe itʼs too late if issues are only discovered by users once the system is in production. Thatʼs why we focus on proactive monitoring, providing tools to detect AI vulnerabilities before they surface in real-world use. This involves continuously generating different attack scenarios and potential hallucinations throughout your AIʼs lifecycle.
  • Accessible for Business Stakeholders:
    Giskard is not just a developer tool—itʼs also designed for business users like domain experts and product managers. It offers features such as a collaborative red-teaming playground and annotation tools, enabling anyone to easily craft test cases.
How does Giskard work to find vulnerabilities?

Giskard employs various methods to detect vulnerabilities, depending on their type:

  • Internal Knowledge:
    Leveraging company expertise (e.g., RAG knowledge base) to identify hallucinations.
  • Security Vulnerability Taxonomies:
    Detecting issues such as stereotypes, discrimination, harmful content, personal information disclosure, prompt injections, and more.
  • External Resources:
    Using cybersecurity monitoring and online data to continuously identify new vulnerabilities.
  • Internal Prompt Templates:
    Applying templates based on our extensive experience with various clients.
Should Giskard be used before or after deployment?

Giskard can be used before and after deployment:

  • Before deployment:
    Provides comprehensive quantitative KPIs to ensure your AI agent is production-ready.
  • After deployment:
    Continuously detects new vulnerabilities that may emerge once your AI application is in production.
After finding the vulnerabilities, can Giskard help me correct the AI agent?

Yes! After subscribing to the Giskard Hub, you can opt for support from our LLM researchers to help mitigate vulnerabilities. We can also assist in designing effective safeguards in production.

What type of LLM agents does Giskard support?

The Giskard Hub supports all types of text-to-text conversational bots.

Giskard operates as a black-box testing tool, meaning the Hub does not need to know the internal components of your agent (foundational models, vector database, etc.).

The bot as a whole only needs to be accessible through an API endpoint.

What’s the difference between Giskard Open Source and LLM Hub?
  • Giskard Open Source → A Python library intended for developers.
  • LLM Hub → An enterprise solution offering a broader range of features such as:
    • A red-teaming playground
    • Cybersecurity monitoring and alerting
    • An annotation studio
    • More advanced security vulnerability detection

For a complete overview of LLM Hub’s features, follow this link.

I can’t have data that leaves my environment. Can I use Giskard’s LLM Hub on-premise?

Yes, you can easily install the Giskard Hub on your internal machines or private cloud.

How much does the Giskard Hub cost?

The Giskard Hub is available through annual subscription based on the number of AI systems.

For pricing details, please follow this link.

Ready. Set. Test!
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Discover how leading organizations secure their AI:
  • Prevent costly AI incidents with expert-validated testing
  • Transform business knowledge into comprehensive test suites
  • Deploy AI with metric-driven confidence