kausable
Category: Foundation Models / LLMs
kausable is a German deep-tech AI startup developing reasoning-first frontier AI models that learn from minimal data and adapt to new situations without requiring retraining, using causal world models trained on synthetic data. kausable was founded in 2025. The company is led by Johannes Haux. Based in Heidelberg, Germany. Team size: 1-10. Total funding raised: $14.9M. Latest round: Seed. Key investors include UVC Partners, Entourage, HTGF (High-Tech Gründerfonds), Mätch VC.
- Founded
- 2025
- Headquarters
- Heidelberg, Germany
- Team size
- 1-10
- Total funding
- $14.9M
Value proposition
Building reasoning-first frontier AI that adapts to changing contexts without costly retraining — a causal world model that learns like humans from just a few examples, trained on synthetic data, enabling adaptation across robotics, energy, finance, and healthcare domains.
Products and solutions
TipPFN (zero-shot forecasting model for complex dynamic systems predicting tipping points and black swan events), Causal world model (reasoning-first foundational AI that learns from minimal data and adapts without retraining)
Unique value
Reasoning-first causal world model that requires no retraining — AI that adapts to new situations from a handful of examples, trained on synthetic causal data rather than massive real-world datasets, enabling sovereign European frontier AI.
Target customer
Enterprises in highly dynamic domains including robotics, energy sector, finance, healthcare, and industrial forecasting — organizations with complex systems that are difficult to predict and control
Industries served
Robotics, Energy, Finance, Healthcare, Industrial forecasting
Technology advantage
Reasoning-first causal world model trained on synthetic data using Bayesian learning; zero-shot transfer across domains; no retraining required; TipPFN model for predicting rare high-impact events; co-authored research with Columbia University validating causal reasoning architecture; founded by three physicists with ties to Heidelberg University and Black Forest Labs
How they differentiate
Unlike conventional AI models that require massive datasets and constant retraining (scaling-law approach), kausable builds reasoning-first causal world models that learn from minimal examples and adapt without retraining. Trained on synthetic causal data rather than real-world data, enabling privacy, control, and cross-domain transfer. Challenges the scaling-law orthodoxy with a Bayesian learning approach.
Main competitors
Sakana AI (Japan - also building alternative AI paradigms), Aleph Alpha (Germany - European sovereign AI), Mistral AI (France - European frontier LLMs)
Key partnerships
Heidelberg University (research roots), Black Forest Labs / BFL (alumni ties), Columbia University (co-authored research paper validating causal reasoning architecture), ELLIS (European Laboratory for Learning and Intelligent Systems)
Major milestones
2024: Initial idea development, 2025: Company incorporation, raised €1.5M pre-seed, 2025: Developed TipPFN zero-shot forecasting model, 2026: Co-authored research paper with Columbia University, 2026-07: Raised €12M seed round led by UVC Partners and Entourage
Market positioning
European sovereign frontier AI lab positioned as an alternative to US/China-dominated AI development. Competes with other frontier AI labs but differentiates on reasoning-first causal approach vs. scaling-law paradigm. Backed by top European VCs as a strategic digital sovereignty play.
Geographic focus
Europe (with emphasis on German/French/EU digital sovereignty)
About Johannes Haux
Ex-researcher at Heidelberg University Computer Vision Group (Prof. Björn Ommer); Co-authored computer vision papers (Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis); background in deep learning and computer vision; physicist by training
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Official website: https://www.kausable.ai