How Much Did Giskard Raise? Funding & Key Investors

Giskard has successfully raised $1.5 million in a single funding round, with notable backing from Elaia, Bessemer Venture Partners, and several angel investors. The platform is dedicated to building the first collaborative and open-source Quality Assurance platform for AI models, ensuring compliance with upcoming AI regulations and standards.

Keep reading to explore the intricacies of Giskard's fundraising journey and the investors backing this innovative platform.

What Is Giskard?

Giskard, founded in 2021, is an early-stage startup based in Paris. The company focuses on Quality Assurance for AI models, aiming to build the first collaborative and open-source platform that ensures compliance with upcoming AI regulations and standards.

Co-founded by Andrei Avtomonov, Jean Marie John Mathews, and Alex Combessie, Giskard helps AI and business teams collaborate to evaluate and test AI models. Their platform increases the efficiency of AI development workflows, eliminates risks of AI biases, and ensures robust, reliable, and ethical AI models.

With a small team of 1-10 employees, Giskard is dedicated to securing AI agents, particularly Large Language Models (LLMs), by providing continuous testing to prevent hallucinations and security issues.

How Much Funding Has Giskard Raised?

1. Amount Raised: $1,500,000
   • Date: December 2022
   • Lead Investors: Elaia, Vincent Huguet, Simon Dawlat, Félix Malfait, Frederik J. Jacobs, Emmanuel Cassimatis, Julien Chaumond, Chris Schagen, Charles Gorintin
   • Motivation Behind the Round: To recruit additional talent, develop and commercialize offerings, and venture into new fields such as computer vision, time series, and generative AI models.

Giskard has raised a total of $1,500,000 with a current valuation of $5 million (~estimated).

Key Investors

• Elaia
  • Details: Elaia is a leading European venture capital firm focused on the digital economy. They have a strong track record of supporting technology startups from early stages to growth.
  • Investment Focus Areas: Digital economy, technology startups, innovation.
  • Notable Investments: Criteo, Mirakl, Shift Technology.
• Bessemer Venture Partners
  • Details: Bessemer Venture Partners is a global venture capital firm that invests in early-stage companies across various sectors. They have a history of backing transformative companies.
  • Investment Focus Areas: Technology, software, healthcare.
  • Notable Investments: LinkedIn, Shopify, Pinterest.
• Julien Chaumond
  • Details: Julien Chaumond is the CTO of Hugging Face, a company known for its contributions to natural language processing. He is an expert in AI and machine learning.
  • Investment Focus Areas: AI, machine learning, natural language processing.
  • Notable Investments: Hugging Face.
• Oscar Salazar
  • Details: Oscar Salazar is the founding CTO of Uber, where he played a crucial role in developing the platform. He is a seasoned entrepreneur and investor in the tech space.
  • Investment Focus Areas: Technology, transportation, mobility.
  • Notable Investments: Uber, Pager, Ride.
• Vincent Huguet
  • Details: Vincent Huguet is the CEO of Malt, a leading freelance marketplace in Europe. He has extensive experience in the gig economy and freelance platforms.
  • Investment Focus Areas: Freelance platforms, gig economy, technology.
  • Notable Investments: Malt.

What's Next for Giskard?

Giskard stands at the cusp of significant opportunities, driven by the increasing demand for responsible AI. With their recent €1.5 million funding, they are poised to expand into new fields like computer vision, time series, and generative AI models. This growth potential is immense, given the rising importance of AI quality assurance in the industry.

As Giskard continues to develop and commercialize their offerings, future fundraising rounds seem likely. The successful initial funding round indicates strong investor confidence, which could pave the way for additional capital to fuel further expansion. However, challenges such as addressing AI-related incidents and building trust in AI systems remain. Ensuring their products effectively mitigate errors and biases will be crucial for sustained success.

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