Freiburg-based Prior Labs, an AI startup innovating foundation models for spreadsheets and databases, has raised €9 million in pre-Seed funding, to accelerate product development, expand the team, and bring the model to more users.
The funding round was led by Balderton Capital along with XTX Ventures, SAP founder Hans Werner-Hector’s Hector Foundation, Atlantic Labs, and Galion.exe. Prominent AI angel investors such as Peter Sarlin (Founder & CEO, Silo AI), Thomas Wolf (Founder & CSO, Hugging Face), Guy Podjarny (Founder, Snyk & Tessl), Ed Grefenstette (Director, DeepMind), Robin Rombach (Founder & CEO, Black Forest Labs), Chris Lynch (Founding Investor Data Robot & CEO, AtScale), Ash Kulkarni (CEO, Elastic) and other business leaders also participated.
Frank Hutter, Co-founder and CEO of Prior Labs said: “Most of the world’s critical decisions are powered by tabular data, yet tools to analyse it are outdated and lacking. We’re bringing a quantum leap to the predictions that businesses can make from their most valuable data and building a future where engaging with tables is as seamless as using AI for text or images. We can deliver faster, more accurate predictions that empower businesses to do more with less.”
Prior Labs was founded in late 2024 from within the ELLIS ecosystem by Professor Frank Hutter, an AutoML researcher; Noah Hollmann, a computer scientist experienced at Google and BCG; and Sauraj Gambhir, a former venture capital, M&A and enterprise growth expert. Bernhard Schölkopf, a leading AI pioneer (Director at ELLIS & Max Planck Institute Tübingen), and entrepreneur and investor Alex Diehl (Co-Founder of Architizer, KKLD, and BMW iVentures) are Prior Labs’ founding advisors.
With 20+ years of experience in machine learning, Hutter‘s team leveraged their expertise to create an advanced foundation model for tabular data. Their work showcases the potential of TabPFN.
Now, Prior Labs is scaling this academic success to deliver real-world impact by integrating their API into business’ data workflows, enabling businesses to unlock the potential of their tabular data.
Tabular data—structured data in tables, spreadsheets, and databases—underpins critical operations in healthcare, finance, environmental monitoring, and manufacturing. Despite its importance, tabular data analysis has lagged behind the rapid advances seen in AI for text and images. Challenges such as messy, diverse, and context-specific data have left businesses reliant on outdated tools or costly, bespoke machine learning models for each task – according to Prior Labs.
Trained on 130 million synthetic datasets, TabPFN is designed to understand and infer patterns in any dataset instantly, without requiring task-specific training. As a foundation model, it also allows fine-tuning with a company’s proprietary data, continuously improving its accuracy and adaptability to real-world challenges.
In a recent Nature paper, TabPFN was shown to outperform the accuracy of state-of-the-art models in over 96% of use cases on small tabular data. It requires 50% of the data to reach the same level of accuracy as the next best model and only takes 2.8 seconds to deliver better performance than the best existing models in 4+ hours.
In data-constrained fields such as healthcare, medicine, and climate science, where acquiring data is often challenging and expensive, TabPFN delivers results with 50% less data.
Latest advancements include support for text features, fine-tuning on proprietary data and the ability to incorporate contextual information about the prediction task further increasing accuracy and ease-of-use.
James Wise, Partner, at Balderton Capital, said: “Tabular data is the backbone of science and business, yet the AI revolution transforming text, images and video has had only a marginal impact on tabular data – until now. Prior Labs’ breakthrough gives everyone the super-powers of machine learning without needing to train their own models on their own data. We’re thrilled to support this world-class team as they redefine how industries unlock the value of their data.”
About Prior Labs: Prior Labs is pioneering a new era in tabular machine learning. Founded in late 2024 by Frank Hutter, Noah Hollmann and Sauraj Gambhir, with Bernhard Schölkopf and Alex Diehl as advisors, Prior Labs’ tabular foundation model (TabPFN) builds on academic research to bring real-world benefits and commercial impact to more companies and use cases worldwide. Delivering unmatched speed, accuracy and efficiency, Prior Labs’ foundation models will transform how businesses unlock insights from their most valuable and complex data.