German AI Startup Prior Labs Secures €9M to Revolutionize Tabular Data Processing

Freiburg-based AI startup Prior Labs has raised €9M in pre-seed funding to develop and expand its innovative approach to tabular data analysis. The funding round was led by Balderton Capital, with participation from XTX Ventures, Hector Foundation, Atlantic Labs, and Galion.exe.

The investment attracted notable AI experts and business leaders, including Thomas Wolf (Founder & CSO of Hugging Face), Peter Sarlin (Founder & CEO of Silo AI), Guy Podjarny (Founder of Snyk and Tessl), Ed Grefenstette (Director at DeepMind), Robin Rombach (Founder & CEO of Black Forest Labs), Chris Lynch (Founding Investor of Data Robot and CEO of AtScale), and Ash Kulkarni (CEO of Elastic).

The funding will enable Prior Labs to advance product development, expand its team, and integrate its technology across a range of industries. James Wise, Partner at Balderton Capital, highlighted the company’s potential to unlock the value of tabular data, which plays a vital role in both business and science. “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,” he said. Wise explained that Prior Labs is providing a solution that allows businesses to harness machine learning without the need to develop task-specific models.

At the core of Prior Labs’ success is its TabPFN model, designed to transform how businesses and organizations analyze structured data found in spreadsheets and databases. Trained on 130 million synthetic datasets, the model can detect patterns across datasets without requiring specialized training for specific tasks. The foundation model supports fine-tuning with proprietary data, enabling it to deliver highly accurate and adaptable predictions for various real-world applications.

According to Frank Hutter, co-founder and CEO of Prior Labs, current tools for analyzing tabular data are outdated and insufficient. He stated, “Most of the world’s critical decisions are powered by tabular data, yet tools to analyze it are outdated and lacking. We’re bringing a quantum leap to the predictions businesses can make from their most valuable data.” The model aims to improve prediction accuracy and reduce the time it takes for businesses to obtain actionable insights from tabular data.

Recent research published in Nature highlights the effectiveness of the TabPFN model, showing that it outperforms “state-of-the-art models” in over 96% of cases when analyzing small datasets. It also requires 50% less data to achieve the same accuracy as competing models and delivers results in 2.8 seconds, compared to the hours required by existing solutions. This speed and efficiency make it suitable for industries such as finance, healthcare, and scientific research, where fast and accurate predictions are essential.

By integrating its API into business workflows, Prior Labs is making it easier for companies to leverage its model for decision-making processes, including trading, business analytics, and scientific exploration. In fields like healthcare and climate science, where data is often limited, the model’s ability to achieve results with less data could lead to significant advancements in research and development.

Founded in late 2024 by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, the company is now focused on refining its technology further. Its plans include improving speed and accuracy, supporting text-based features within datasets, and enabling fine-tuning with contextual data for even more precise predictions. As it continues to scale, Prior Labs aims to redefine how businesses interact with their most valuable data assets.