New German AI research lab Grubel raises €3 million to build systems that adapt to individual legal cases

Grubel, a Munich- and Tübingen-based AI research lab developing systems that adapt to individual legal matters, has raised €3 million in pre-Seed funding to support research, product development, and team expansion.

The round was led by venture capital firm Point Nine, with participation from angel investors including Jeff Dean (Google’s former chief scientist), Chris Ré (Stanford professor and Together AI co-founder), Ion Stoica (UC Berkeley Professor and Databricks co-founder), and Harvey co-founders Gabe Pereyra and Winston Weinberg, among others.

“Complex legal matters do not come with ready-made training data, task environments, or clear tests of success. We are automating the AI specialisation loop that identifies the right information, curates it into training data and environments, adapts the system to the matter, and tests whether its work meets the required standard,” said Reinhard Heckel, co-founder of Grubel. 

Founded in 2026 by machine-learning researchers Moritz Hardt and Heckel, Grubel is automating AI specialisation: gathering and preparing relevant information, adapting the system to the legal work at hand, and testing whether its work meets the required standards.

Hardt is a director at the Max Planck Institute for Intelligent Systems and a former UC Berkeley professor and Google Brain researcher. He has co-developed test-time training and co-authored Lawma. Heckel is a TUM professor of machine learning, currently on leave and formerly a researcher at IBM Research. He focuses on data-centric machine learning and helped create DataComp-LM and OpenThoughts. 

According to the company, complex knowledge work beyond coding remains surprisingly dependent on highly skilled human labour, with AI still playing only a limited role. Grubel opines that this gap is particularly apparent in legal services.

The company notes that legal AI has lagged in part because complex legal work is both highly specialised and very difficult to evaluate. It often requires expertise specific to an individual matter, including its facts, documents, terminology, prior work product, and client standards. 

It further notes that today, teams of engineers and lawyers build specialised AI systems for specific legal workflows by curating data, adapting the systems, and evaluating their performance. Although this specialisation is powerful, it requires substantial manual effort. It states that an engineering team cannot build and maintain a separate system for every legal matter. 

Grubel claims to be automating that specialisation process. Its thesis is that every matter needs its own AI. To make that possible, the company is automating the process of specialising an AI system for each matter. Its specialisation loop has three components: a data engine that discovers and curates task-relevant data for each matter or client; a test-time adaptation layer that adapts both the model and the agent to the matter before acting; and a continual evaluation framework that checks the work against standards derived from the matter itself. The loop repeats until the system delivers what the legal task demands. 

It’s bringing data curation, adaptation, and evaluation into a specialisation loop that trains and improves on the matter itself.

“Over the past two to three years, we’ve seen many legal AI companies emerge, but few are pushing the boundaries of AI research in the legal field as ambitiously as Reinhard, Moritz and the Grubel team. We believe their approach can unlock new capabilities in legal AI and, over time, other forms of complex knowledge work. We’re excited to support them on this journey,” said Louis Coppey, Partner at Point Nine.

The company plans to use this capital to support its research, product development, and team expansion. An initial evaluation showed that Grubel’s specialisation loop can improve performance over general-purpose frontier systems on the legal tasks tested. The company aims to further develop its automated specialisation loop to improve performance on complex legal work while allowing more routine tasks to be handled by smaller, more efficient models.

The post New German AI research lab Grubel raises €3 million to build systems that adapt to individual legal cases appeared first on EU-Startups.