EquiLibre Technologies, based in Prague and started by three former DeepMind researchers, has completed a Series A funding round led by Creandum, bringing its valuation to over $500 million.

Creandum says this is the largest investment it has ever made in one company.

EquiLibre says its AI agents trade billions of dollars each day on the S&P 500 and Nasdaq, but other companies also claim to have similar technology, challenging EquiLibre’s claim to be first.

The founders of EquiLibre Technologies, who previously built an AI that beat professional poker players, are now using their skills in financial markets. Martin Schmid, Rudolf Kadlec, and Matej Moravcik started the company in Prague in December 2021.

EquiLibre just closed a Series A round led by Creandum at a valuation of over $500 million. Creandum has not shared the size of the round, only the valuation and that it is their largest investment in a single company.

“The market is the judge, and the verdict updates every millisecond. That’s what drew us to the problem, and it’s why reinforcement learning is such a natural fit. The question is no longer whether this approach works. It’s how big it can get. We’ve proven the technology in the world’s biggest and most liquid markets,” says Schmid, EquiLibre’s CEO.

The case for reinforcement learning in trading

Schmid, Kadlec, and Moravcik left DeepMind to create DeepStack, the first AI to beat professional players at no-limit poker. They used reinforcement learning, training the model through trial and reward instead of labelled examples, in a competitive, high-stakes setting with clear scoring. Schmid says markets are similar.

The founders returned from North America to start the company in Prague, thinking it would be easier to retain talented people than in Silicon Valley.

“We want to build a global business from Prague. We are by far the most exciting company working on the frontier of applied AI research here, and this allows us to attract and retain amazing talent. Our ambitions are global, and we aim to build the world’s leading AI trading company,” Schmid says.

This choice helped EquiLibre build a team of about 25, mostly Czech engineers who have worked at Google and other US tech companies.

EquiLibre trains reinforcement learning agents using both historical and live market data, then lets them trade on their own. After trying its approach on crypto markets in 2025, EquiLibre moved into US stocks. Its agents now trade billions each day on the S&P 500 and Nasdaq with Tower Research Capital, a detail not included in EquiLibre’s funding announcement.

The “first” problem

EquiLibre says it is the first AI lab to use reinforcement learning live on US markets. However, this is hard to confirm and is different from what Jane Street, a top trading firm, has said.

Jane Street uses reinforcement learning with LLMs, or whatever else we need to train good models, and has tens of thousands of high-end GPUs, much more than EquiLibre. Schmid sees this as a strength, highlighting EquiLibre’s focus on efficiency.

The company says it has not had any negative months since it started, but it has not shared the time period, methods, or drawdown data.

Cameron Sellers, vice president at Creandum, says the firm is focused more on scaling than on being first: “This is the largest investment we have ever made, showing the belief that we have in the future scaling of the technology. EquiLibre is doing what the best frontier labs do: picking a domain where the feedback loop is brutal and honest, and letting the technology speak for itself. Martin and the team have proven the approach in the hardest possible environment. We’re thrilled to back them as they scale.”

Creandum regularly backs ambitious European founders and urges them not to undersell themselves compared to their American peers.

Where EquiLibre sits against the competition

Quantitative strategies now make up a larger share of hedge fund assets. More AI-focused funds are replacing traditional statistical methods. Two Sigma and Renaissance Technologies are examples of firms built on years of statistical modelling, while Citadel Securities has invested a lot in machine learning at a scale most startups cannot match.

In this setting, EquiLibre stands out not for its computing power or history, but for its research-driven approach inspired by DeepMind and its focus on reinforcement learning as the best method for markets.

This round comes after a $10 million seed led by Blossom Capital at a $140 million valuation and an earlier pre-seed from Credo, a Central and Eastern Europe-focused fund that also invested early in ElevenLabs and UiPath.

Most of the new money will go toward computing resources. EquiLibre plans to build one of the largest compute clusters in Central and Eastern Europe and keep growing its research and engineering team in Prague.

Schmid believes the market can support several successful companies. With the size of global markets, this seems true for now. But as larger firms with more resources adopt similar reinforcement learning strategies, EquiLibre’s claim of being first may matter less than its ability to stay ahead.