Paris-based AI company Arlequin AI has raised €28 million in Series A funding to accelerate the development of its topological neural network architecture and expand the international deployment of its technology. The round was co-led by redalpine and OTB Ventures, with participation from Bpifrance’s Defence Innovation Fund. Existing investors Vsquared Ventures and 10x Founders increased their stakes, while Xavier Niel also joined the round.

Founded in 2024 by Hugo Micheron, a specialist in terrorism studies and geopolitical instability, and Antoine Jardin, a former CNRS research engineer specialising in data science and human behaviour, Arlequin develops AI systems for organisations making consequential decisions from large and fragmented datasets.

The company’s platform analyses relationships across different types of information, including documents, transactions, video and operational data. It is designed to help users identify connections within complex datasets while allowing them to trace results back to the underlying evidence.

Arlequin is now developing proprietary models based on topological neural networks, an AI architecture designed to learn from both data points and the relationships between them. The company says the approach can capture complex interactions involving multiple elements at once, enabling analysis of systems that become difficult to interpret as datasets grow in scale.

Today, another revolution is taking shape: the development of new AI systems capable of understanding highly complex dynamics hidden within millions of data points,

said Hugo Micheron, CEO and co-founder of Arlequin AI.

Arlequin sees potential applications in security and defence, criminal investigations, fraud and money-laundering detection, information integrity, cybersecurity, and AI safety and security. Its technology is already being used by governments and large organisations across Western and Eastern Europe.

Antoine Jardin, CTO and co-founder, said the company believes further AI advances will require different architectures rather than simply larger models trained on more data and computing power. Arlequin’s approach is also intended to require less compute than large-scale AI systems, reducing its reliance on energy-intensive infrastructure and advanced semiconductors.

The new capital will be used to expand the international team developing and training its proprietary models, and to support commercial deployment in Europe and internationally. The company has opened offices in London and Berlin and plans to establish an AI lab in Silicon Valley in the coming months.