Wiremind Raises €35 Mn in First-Ever Funding Round to Expand Globally and Boost AI Development

The funding round brings in three new investors: IronWave, formerly known as Wendel Growth; Bpifrance Investissement through its Large Venture fund; and Seaya Andromeda.

Despite the new investment, Wiremind’s co-founders, CEO Colin Girault-Matz and CTO Charles Pierre, will remain the company’s majority shareholders and continue to control the business.

“Charles and I began our careers more than fifteen years ago in the rail and aviation industries, so we have a unique understanding of the challenges our clients face. It is this first-hand knowledge that we leveraged to grow Wiremind over the years, while staying completely self-financed and investing in our own R&D,” said Girault-Matz, co-founder and CEO, Wiremind.

He added, “Today, we’re accelerating our efforts given the speed at which the market is adopting artificial intelligence tools. This financing round will allow us to reach rapidly growing geographic markets where decisions on technology investments are being made right now.”

“In twelve years, and without a single euro of outside capital, Colin, Charles and their teams have built systems that today drive the revenue of the largest rail and air operators,” said Victoire Laurenty, founder and General Partner of IronWave (formerly Wendel Growth).

The funding will help Wiremind expand into new international markets and invest more in AI research and development. The company aims to strengthen its presence in South America while entering more markets in the Middle East and Asia-Pacific (APAC).

Wiremind will build on its existing success in Europe and North America as it expands its global reach and develops its AI technology further.

Wiremind was founded in October 2014 and has grown without outside funding until now. The company was started by founders who share an interest in data visualisation, technology and optimisation.

The founders first met while working at SNCF, France’s national railway company. During that time, they saw a need for machine learning tools that could help transport operators manage revenue and capacity more efficiently.

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