Aisot Technologies AG, the Zurich-headquartered, ETH Zurich spin-off building AI-driven portfolio intelligence tools for institutional investors, has wrapped up a CHF2m extension to its seed round.
The additional capital came from a mix of existing backers and newcomers, including family offices and seasoned angel investors. Among the fresh names on the cap table is Felix Haldner, who previously held a partner role at Partners Group and once led the Swiss Funds & Asset Management Association (SFAMA) as its president.
Aisot Technologies builds AI-based tools aimed at asset and wealth managers, as well as family offices, with a focus on portfolio optimisation and risk management.
Founded in 2021 as an official spin-off from ETH Zurich, the firm is based in Zurich and works with a network of partners and staff spread across Europe, Asia and the US, drawing on specialists in artificial intelligence, natural language processing and financial mathematics.
Its platform combines financial analysis, machine learning and LLM-based news sentiment analysis to offer deployable forecasting and decision-making models. Large asset managers can identify additional signals, while smaller managers can benefit from access to professional data, AI and quant infrastructure without needing to build it.
Aisot Technologies co-founder and CEO Stefan Klauser said, “The fact that our existing investors continue to expand their trust, while we’ve also gained experienced figures like Felix Haldner as new supporters, confirms we’re on the right path.
“We want to help wealth managers in Switzerland and internationally to integrate AI into their investment processes in a way that is well-founded, transparent and impactful.”
New investor Felix Haldner said, “We are convinced that aisot’s solutions can generate significant efficiency gains for asset and wealth managers, including in analysis, portfolio management and product development.
“Aisot Technologies’ models also deliver signals across different investment strategies, with the aim of contributing to return optimization.”
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