CHF 2m · Seed · Fintech · Zurich, Switzerland
Zurich-based Aisot Technologies has raised a CHF 2 million seed extension to develop its AI portfolio-intelligence platform and expand adoption among Swiss and international asset managers. The company said the round brought together existing and new investors, including family offices and angel backers, and named Felix Haldner as a new investor.
Putting AI inside institutional investment workflows
Aisot builds software for institutional investment teams. Its platform combines quantitative analysis, machine learning and large-language-model-based news sentiment to support forecasting, portfolio construction and personalisation. The company positions the product as an intelligence layer for asset managers, wealth managers and family offices that want to use more data and AI without assembling the full infrastructure and specialist team internally.
That matters because investment firms do not buy forecasts in isolation. They need outputs that can fit established research, risk and portfolio-management processes, with enough transparency for people to challenge the result. Aisot's commercial task is therefore broader than improving its models: it must make the product useful within the controls, data environments and decision routines of institutional customers.
The financing extends an earlier CHF 1.8 million seed round completed in 2023, according to investor Haute Capital Partners. Haute, which has backed Aisot since that round, says the company was founded in 2021. The new extension is intended to give the team more room to strengthen the platform and widen distribution rather than marking a separate later-stage raise.
Distribution and trust are the next test
For larger asset managers, the attraction is the ability to test more signals and tailor portfolios without replacing the investment process around them. For smaller firms, a hosted platform can reduce the cost of accessing quantitative data, machine-learning tooling and computing infrastructure. Those customer groups create different demands, however: enterprise deployments depend on integration and governance, while smaller teams need a product that is usable without extensive implementation support.
The extension round therefore puts the emphasis on execution. Aisot must show that its combination of quantitative models and language-based sentiment produces repeatable value across market conditions, while earning the trust needed for institutional adoption. Follow-on support from existing investors can fund that work, but wider uptake among asset managers will be the clearest test of whether the technology becomes part of recurring investment workflows.
