Giskard AI Review: Secure AI Model Testing

About Giskard

Understanding Giskard: Purpose, Audience, and Solutions

Giskard is an open-source framework designed for testing and managing machine learning models, particularly those involving AI technologies. Its primary purpose is to enhance the reliability and safety of AI systems by providing tools that automate the detection of risks associated with AI applications, such as bias, hallucination, and security vulnerabilities. The target audience for Giskard includes data scientists, AI engineers, and organizations that leverage AI in critical operations. By addressing the challenges of AI model evaluation and compliance, Giskard helps businesses ensure their AI solutions are robust and trustworthy.

Giskard’s Usability and Features: An Overview

Giskard offers a user-friendly platform that simplifies the process of testing AI models. Key features include:

• Automated Testing: Giskard can execute numerous tests on AI models without requiring continuous human oversight.
• Risk Detection: The platform identifies issues such as performance inconsistencies and ethical risks, generating detailed reports on vulnerabilities.
• Collaborative Environment: Giskard encourages teamwork through its interactive dashboard, allowing teams to visualize and analyze testing results.
• Integration Capabilities: Easily integrates with CI/CD pipelines, streamlining the deployment and testing processes for AI applications.

Why Choose Giskard? Unique Selling Points

Giskard stands out in the competitive landscape of AI testing tools due to several key advantages:

• Open-Source Accessibility: Being open-source allows for community contributions and continuous improvement of the platform.
• Comprehensive Risk Management: It addresses all dimensions of AI risks, including quality, security, and compliance, making it a holistic solution.
• Customizable Test Suites: Users can generate domain-specific tests automatically, tailored to their unique AI models and applications.
• Strong Community Support: With an active user community, Giskard benefits from shared knowledge and collaborative problem-solving.

Ideal User Groups and Use Cases for Giskard

Giskard is best suited for a variety of user groups, from beginners to experienced professionals. Below is a table outlining these ideal users and specific use cases where Giskard excels:

User Group	Use Case Example
Data Scientists	Testing and validating predictive models for accuracy.
AI Engineers	Continuously monitoring LLMs for hallucinations.
Compliance Officers	Ensuring AI models meet regulatory standards like the EU AI Act.
Startups	Quick deployment of AI solutions with automated testing.
Large Enterprises	Managing and mitigating risks in complex AI systems.

Giskard excels in scenarios where rapid iterations and robust testing are critical, such as in developing AI applications for healthcare, finance, or any industry where compliance and ethical concerns are paramount. With its comprehensive features and focus on risk management, Giskard empowers organizations to deploy AI with confidence.

Features

Reporting Capabilities Explained

Giskard.ai offers robust reporting capabilities that enable businesses to monitor and evaluate their AI models. These features include:

• Key Performance Indicators (KPIs): Metrics such as concept drift and business-oriented indicators allow actionable insights.
• Continuous Testing: Automated testing methods identify vulnerabilities like hallucinations.
• Automated Alerts: Real-time notifications combat “dashboard fatigue” and highlight performance issues.
• Cross-Team Collaboration: Tools that bring business experts into the testing process.
• Integration into Deployment Pipelines: Automated tests ensure continuous monitoring.
• Risk Detection and Prevention: Specific tests for AI risks including hallucinations.
• Exhaustive Dashboards: Visibility into metrics and health statuses.

Key Integrations Enhancing Functionality

Giskard.ai integrates with:

1. CI/CD Pipelines: Automates testing and logging.
2. MLflow and Weights & Biases: Tracks experiments and model versions.
3. LiteLLM: Simplifies large language model testing.
4. RAGET Toolkit: Evaluates retrieval-augmented generation systems.

These integrations improve workflow by automating processes, ensuring quality, and reducing manual testing.

Mobile App Availability

Currently, Giskard.ai does not offer mobile apps. The platform’s focus is on desktop and enterprise-level applications.

Single Sign-On (SSO) Feature

There is no publicly available information confirming SSO support in Giskard.ai. Users may need to contact their support team for clarification.

Automation Features Overview

Key automation features include:

• Automated Vulnerability Detection: Identifies issues such as hallucinations.
• Test Generation: Creates domain-specific tests.
• CI/CD Integration: Ensures automated continuous testing.
• Open-Source Library: Allows extensive test creation.
• Holistic Testing: Addresses quality, security, and compliance.

Security Measures and Compliance

Giskard.ai employs:

• Data Isolation and Encryption: Hosted in the EU for GDPR compliance.
• AI Risk Mitigation: Continuous testing combats vulnerabilities.

The platform complies with GDPR, ensuring data protection and privacy.

API Capabilities

Giskard.ai provides an API with:

1. LiteLLM Integration: Supports multiple LLM providers.
2. Customization: Tailored tests and configurations.
3. Testing Enhancements: RAGET and vulnerability assessments.
4. Reduced Maintenance: Handles updates across API providers.

Deployment Options

Giskard.ai supports:

1. Cloud-Based:
   • Pros: Scalable, cost-effective, less maintenance.
   • Cons: Data security concerns, internet dependency.
2. On-Premises:
   • Pros: Enhanced control, customization.
   • Cons: High setup costs, maintenance burden.

Pros and Cons of Giskard.ai

Pros:

• Transparency and open-source tools.
• Comprehensive risk management.
• Enhanced collaboration tools.
• Flexible pricing and community support.

Cons:

• Dependency on community contributions.
• Integration complexity.
• Learning curve for new users.
• Market competition.

This detailed analysis covers all aspects of Giskard.ai based on the requested criteria.

Location

Locations and Branches

Company Name	Headquarters Address	Country	Additional Branch Locations
Giskard	24 Rue de la Paix, Pantin	France	None found

Giskard is recognized for its AI services, particularly in ensuring the quality of AI models through a collaborative and open-source software platform.

History and Team

Year Founded:

Giskard was founded in 2021.

Number of Employees:

The company currently has 24 employees.

Team:

Below is the table detailing the founders of Giskard and their respective roles:

Name	Position
Alex Combessie	Co-founder & Co-CEO
Jean-Marie John-Mathews	Co-founder & Co-CEO
Matteo Dora	Chief Technology Officer

Pricing

Business Model of Giskard.ai

Giskard.ai is a software solution designed to improve the quality and trustworthiness of AI systems. Their business model revolves around offering tools for testing, evaluating, and monitoring AI models, particularly in the areas of fairness, performance, and reliability.

Pricing Plans

Giskard.ai operates as a SaaS (Software as a Service) company, providing various pricing plans tailored to the needs of different users and organizations. Below is a detailed breakdown of their pricing plans:

Plan Name	Features	Price
Free Tier	Limited access to Giskard Hub Suitable for individual users Basic support	– Free
Team Plan	Access for up to 5 AI agents Unlimited versions and users Advanced support	– Contact for pricing
Enterprise Plan	Annual subscription for unlimited AI agents Unlimited versions and users Dedicated support and training	– Contact for quote

Notes:

• The Free Tier is ideal for individual users or small-scale projects, offering essential functionalities at no cost.
• The Team Plan is designed for small to medium-sized teams that require more resources and advanced support.
• The Enterprise Plan is targeted at larger organizations with extensive needs, including dedicated support and training.

For all tiers, Giskard.ai emphasizes the importance of contacting their team for tailored pricing and additional details to suit specific requirements.

This pricing model provides flexibility and scalability, allowing individuals and organizations to choose a plan that aligns with their needs and budgets.

Funding and market

Industry

Giskard.ai operates in the Artificial Intelligence (AI) and software development industry, specifically focusing on providing a testing platform for artificial intelligence systems. Founded in 2021 and based in Paris, the company aims to ensure the quality, security, and compliance of AI models. Their platform is designed for enterprise teams, allowing them to manage risks associated with AI technologies effectively.

The industry Giskard.ai is part of encompasses several key aspects:

• Artificial Intelligence (AI) and Machine Learning (ML): Leveraging machine learning algorithms to enhance the performance and reliability of AI systems. Their offerings include tools that help in the testing and validation of AI models to prevent issues such as bias and performance degradation.
• Software Development: Providing open-source solutions for AI quality assurance, which is critical in a data-driven environment.
• Quality Management: Offering a holistic testing environment for AI systems to monitor and evaluate their performance, ensuring safety and compliance.
• Risk Management and Compliance: Helping enterprises manage risks and adhere to regulatory standards associated with AI deployment.
• Enterprise Solutions: Trusted by major enterprises such as AXA and Société Générale, indicating a strong presence in sectors requiring high reliability and security.

Market

Giskard.ai operates in the rapidly expanding Artificial Intelligence (AI) software market. This market is projected to reach approximately $98 billion in 2024 and grow at a compound annual growth rate (CAGR) of 30%, reaching an estimated size of $391.43 billion by 2030. The market’s growth is driven by advancements in generative AI, machine learning (ML), and natural language processing (NLP).

Giskard.ai specifically focuses on tools for testing and validating AI models, addressing critical issues such as hallucinations and security concerns. Their solutions cater to enterprise AI teams and align with the industry’s increasing reliance on AI for business operations.

In terms of market share, the top players in the AI software industry, including NVIDIA, Google, OpenAI, and Microsoft, collectively account for approximately 19% of the market. Giskard.ai, as an emerging player, leverages its niche focus on AI quality assurance to carve out a position in this competitive landscape.

Funding

Giskard.ai has raised a total of €1.5 million in funding through private investment rounds. The funding details are as follows:

• Funding Round: Giskard raised €1.5 million in its first funding round.
• Investors: The round was led by Elaia, with participation from Bessemer Venture Partners and notable angel investors such as Julien Chaumond, Oscar Salazar, Christine Balagué, and others.

This funding is being utilized to scale their operations, expand their team, and enhance their platform’s capabilities.

Stocks

Giskard.ai is not a publicly traded company and does not have stocks or a ticker symbol. They remain a private company that has raised funding through private investment rounds.

Latest news

Latest News and Developments About Giskard.ai

Giskard Leads GenAI Evaluation in France 2030’s ArGiMi Consortium

Read the complete article here
Giskard has been selected as a key player in the ArGiMi consortium, under the France 2030 program. This consortium, which includes Artefact and Mistral AI, is focused on developing next-generation French Large Language Models (LLMs) for businesses.

• Objectives of the ArGiMi Consortium:
  • Mistral AI will develop new LLMs.
  • Artefact will integrate these models into businesses.
  • Giskard will ensure model quality, security, and conformity.
• Commitment to Open-Source:
  

  The initiative is committed to open-source methodologies, aiming to make AI developments transparent, ethical, and accessible across industries.

  
  
• Key Deliverables:
  • Development of new state-of-the-art open-source French LLMs for enterprise use cases.
  • Creation of evaluation methods for LLMs to ensure reliability and safety.
  • Development of deployment tools for easier model integration and fine-tuning.

This project reinforces France’s leadership in responsible AI innovation.

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Giskard Announces “Phare,” a New LLM Evaluation Benchmark

Read the full announcement here
During the Paris AI Summit, Giskard launched “Phare,” a benchmark designed to evaluate Generative AI models on dimensions like hallucination, factual accuracy, bias, and potential harm.

• Collaboration with Google DeepMind:
  

  Phare is being developed in partnership with Google DeepMind, focusing on transparent and independent assessments of AI models.

  
  
• Key Features of Phare:
  • Multi-lingual Design: Covers English, French, and Spanish, incorporating cultural contexts.
  • Independence: Maintains autonomy in benchmark design to ensure unbiased evaluation.
  • Integrity: Uses a protected hold-out dataset for independent testing.
  • Reproducibility: Open-sourcing samples for community verification.
• Future Plans:
  • Expand to more languages and include additional evaluation modules for bias, harmful content, and other reliability aspects.
  • Benchmarking for top AI labs like OpenAI, Anthropic, Meta, and others.

Phare aims to increase trust in AI systems through rigorous and transparent evaluation.

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Monthly Updates from Giskard

Explore more news here
The latest beta release from Giskard introduces features for scanning AI models to detect vulnerabilities directly within development notebooks, further enhancing the security and reliability of AI systems.

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These updates highlight Giskard’s active role in advancing AI safety, quality, and transparency through innovative projects and collaborations.

Search Trends

Search Volume Analysis for Giskard.ai

Table of Search Volumes for Relevant Keywords

Keyword	Search Volume	Competition	Competition Index	CPC (Cost Per Click)	Low Top of Page Bid	High Top of Page Bid
Giskard	480	LOW	0	None	None	None
Giskard.ai	50	LOW	6	None	None	None
AI compliance	720	MEDIUM	36	17.35	6	25
AI testing platform	None	None	None	None	None	None
AI risk detection	None	None	None	None	None	None
AI security	2400	MEDIUM	54	26.77	9.16	27.29
AI model compliance	None	None	None	None	None	None
LLM testing	210	LOW	12	6.69	2.47	6.37
AI regulation compliance	110	LOW	17	18.24	6.45	23.6
Responsible AI	2900	LOW	28	12	3.07	15
AI fairness	390	LOW	1	None	2.3	5.75
AI model security	70	LOW	33	29.52	6.49	24.18

Understanding the Trends in Popularity for Giskard.ai

The search volume trends for Giskard.ai can be attributed to the following recent developments and initiatives:

• Launch of the “Phare” LLM Benchmark:
  • Giskard.ai introduced a new open and multilingual LLM Benchmark called “Phare” at the Paris AI Summit.
  • This benchmark evaluates critical AI security dimensions such as hallucination, factual accuracy, bias, and potential harm across multiple languages.
  • The initiative, in partnership with Google DeepMind, emphasizes creating trustworthiness in Generative AI models through open measurement and evaluation.
• Leadership in the ArGiMi Consortium:
  • Giskard.ai plays a significant role in the ArGiMi Consortium under the France 2030 project.
  • In collaboration with Artefact and Mistral AI, this consortium focuses on developing next-generation French large language models (LLMs) for businesses.
  • The consortium emphasizes AI safety, model quality, and security, further boosting Giskard.ai’s visibility and reputation.

These initiatives and partnerships have garnered significant attention, leading to increased search activities and interest in the company.

For further details, refer to the following URLs:

• Giskard Knowledge
• Monthly News
• Phare Benchmark Announcement

Review

Customers

Giskard AI software is utilized by various notable companies to enhance their AI operations and ensure the reliability and security of their models. Here are detailed examples of organizations leveraging Giskard AI:

1. L’Oréal: Giskard has partnered with L’Oréal to improve their facial landmark detection models. This enhancement is crucial for applications such as face reconstruction and emotion recognition, allowing L’Oréal to better understand customer interactions and improve customer service through more sophisticated AI-driven applications.
2. BNP Paribas: The financial giant is involved in an AI for efficiency program, utilizing Giskard’s capabilities to streamline processes within their BCEF entity. This program aims to accelerate the transformation of their operations by implementing AI solutions that are robust and secure, thereby effectively managing and mitigating risks associated with AI deployment.
3. Artefact, Robertet, and the ArGiMi Consortium: Giskard is also part of the ArGiMi Consortium, which includes Artefact and Mistral AI. This consortium was awarded by Bpifrance for their GenAI projects. Through this collaboration, Giskard’s testing framework helps these organizations evaluate AI models thoroughly before they are pushed into production, ensuring they are free from biases and security vulnerabilities.
4. TechCrunch Insights: According to a recent article on TechCrunch, Giskard is recognized for its open-source framework that evaluates AI models pre-deployment. This framework alerts developers to possible biases and security holes, which is particularly critical given the increasing use of AI models in enterprise applications. This proactive approach aids organizations in maintaining high standards of AI safety and reliability.
5. AI Red Teaming: Giskard’s approach also includes AI Red Teaming, which focuses on detecting safety and security breaches in LLM applications. This involves developing threat models and realistic attack scenarios to identify vulnerabilities, ensuring that organizations can protect their AI systems from potential threats effectively.

Alternatives

Software	Features	Pricing	Target Audience
Giskard AI	– AI model testing platform – Interactive dashboards – Compliance tracking – Collaboration tools for red-teaming – Usage-based pricing	– Subscription-based pricing – Enterprise annual subscription – Custom pricing available	Enterprises with AI projects needing compliance, security, and testing solutions
Verta	– Model management – Data governance – Deployment tracking – Collaboration tools	– Custom pricing based on usage	AI and ML developers, Data scientists in enterprise environments
Pezzo	– Prompt management – Performance monitoring – Troubleshooting tools – Team collaboration support	– Free trial available – Pricing based on features used	Teams focusing on AI development and deployment
Deepchecks	– ML model testing – Data validation – Performance monitoring – Dashboard for insights	– Pricing varies by deployment size – Free tier available	Data scientists and ML engineers looking for model validation tools
Langtail	– Low-code testing platform – User-friendly interface – Comprehensive testing features	– Freemium model – Advanced features at a cost	Startups and small teams working on AI applications

Comparing FlowHunt with Giskard

Both FlowHunt and Giskard provide tools for AI model evaluation and customization. While Giskard focuses on AI quality assurance and vulnerability scanning, FlowHunt allows users to achieve similar outcomes by leveraging its modular architecture to build, test, and deploy custom LLMs and AI tools effortlessly. FlowHunt simplifies the process with its ready-made templates and integrated AI library, eliminating complex setups and promoting seamless AI integration.