SUPERPOSE
The combinatorial optimization engine for AI data
Intelligent data selection for robotics foundation models. We help teams building general-purpose robots optimize their training data for better coverage, fewer redundancies, and faster iteration.
WHAT WE BUILD
Three pillars of Superpose
We develop core optimization technology, apply it to robotics data as our first product, and invest in long-term quantum research.
CORE TECHNOLOGY
In DevelopmentCombinatorial Optimization Engine
Mathematical optimization for intelligent data selection. Select the highest-value data with coverage guarantees.
PRODUCT
Active FocusRobotics Data Engine
Dataset curation for robotics foundation models. Better coverage, fewer redundancies, faster iteration cycles.
RESEARCH
R&DQuantum Algorithms
Quantum-native computing for practical AI applications. Bridging theoretical physics with real-world optimization.
THE CHALLENGE
Training data bottlenecks in robotics AI development
Building general-purpose robots and robotics foundation models requires massive amounts of diverse, high-quality training data. Yet as datasets scale, a fundamental challenge emerges: not all data is equally valuable.
Training runs often include significant data overlap, with similar scenarios captured repeatedly, while critical edge cases and challenging failure modes remain underrepresented. Traditional manual curation becomes impractical at scale, creating a bottleneck between data collection and model improvement.
OUR APPROACH
Intelligent data selection through optimization
Superpose treats dataset curation as an optimization problem. You define what matters for your specific use case: whether that's maximizing coverage of edge cases, ensuring safety-critical scenarios are well-represented, or balancing diversity across operational domains.
Our software analyzes your full dataset and formulates the selection problem mathematically, accounting for your training objectives, budget constraints, and compute resources. The optimization engine then identifies the subset of data that will deliver the highest marginal value for your next training run.
OUR TEAM
Built by researchers, for researchers
Our founding team brings together PhDs in Physics and Computer Science from UC Berkeley, with prior experience at Apple, Meta, and NVIDIA. We combine expertise in high-energy physics, machine learning, and autonomous robotics.
Get Started
Let's discuss how Superpose can accelerate your model development