Freiburg-based AI startup Prior Labs has raised €9 million in a pre-seed funding round to advance its AI-driven foundation models designed for tabular data analysis. The round was led by Balderton Capital, with participation from prominent investors, including XTX Ventures, Hector Foundation, Atlantic Labs, and Galion.exe.
The funding also attracted notable AI industry leaders such as Thomas Wolf (Founder & CSO, Hugging Face), Peter Sarlin (Founder & CEO, Silo AI), Guy Podjarny (Founder, Snyk & Tessl), Ed Grefenstette (Director, DeepMind), Robin Rombach (Founder & CEO, Black Forest Labs), Chris Lynch (Founding Investor Data Robot & CEO, AtScale), and Ash Kulkarni (CEO, Elastic), among others.
Bridging the AI Gap in Tabular Data:
Despite significant AI advancements in text, images, and video, tabular data—which forms the backbone of industries such as healthcare, finance, and manufacturing—has not experienced the same level of transformation. Prior Labs aims to change this narrative with its pioneering AI model, TabPFN, which is built to revolutionize structured data analysis.
James Wise, Partner at Balderton Capital, emphasized the importance of this development, stating: “Tabular data is the foundation of business and science, yet AI has had only a marginal impact in this space—until now. Prior Labs’ technology empowers businesses to harness machine learning capabilities without the complexity of training models from scratch. We are excited to support this world-class team as they redefine how industries unlock the value of their data.”
What Makes Prior Labs Unique?
At the core of Prior Labs’ innovation is its foundation model, TabPFN, a groundbreaking AI-powered tool trained on 130 million synthetic datasets. Unlike traditional machine learning models, TabPFN does not require extensive task-specific training. Instead, it can analyze any dataset, identifying patterns and making accurate predictions with minimal input.
This advancement has profound implications for industries that rely on structured data, such as finance, trading, business analytics, healthcare, and climate science. A recent study published in Nature found that TabPFN outperformed existing state-of-the-art models in over 96% of cases involving small datasets. Furthermore, it achieved equivalent accuracy with 50% less data and processed results within 2.8 seconds, compared to four hours required by traditional methods.
Transforming Business and Scientific Decision-Making
By reducing data requirements and improving prediction speed, TabPFN enhances business forecasting, trading strategies, and financial analytics. Its ability to deliver high-accuracy predictions with limited data makes it a game-changer in fields like medicine and climate science, where data constraints often limit research capabilities.
Frank Hutter, co-founder and CEO of Prior Labs, highlighted the impact of the company’s technology: “Most of the world’s critical decisions are based on tabular data, yet existing tools are outdated. Our technology represents a quantum leap, making data-driven predictions faster, smarter, and more accessible. Our goal is to make interacting with tabular data as seamless as using AI for text or images.”
Next Steps for Prior Labs:
Founded in late 2024 by Professor Dr. Frank Hutter, Noah Hollmann, and Sauraj Gambhir, Prior Labs brings together over 20 years of expertise in machine learning. With this new funding, the company aims to scale its impact by integrating TabPFN’s API into enterprise data workflows, making advanced AI-driven analysis easily accessible to businesses.
The company is also enhancing its model by improving speed, accuracy, and efficiency while incorporating support for text features and proprietary data fine-tuning. Additionally, they plan to integrate contextual information into predictions, further refining AI-powered tabular data analysis.
With its latest funding and strong investor backing, Prior Labs is poised to redefine how businesses and researchers leverage AI for structured data, driving innovation and efficiency across multiple industries.
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