Data Intelligence for Physical AI
Physical AI models don’t fail for lack of data hours. They fail because the data they trained on never confronted the states that matter.
The scarce resource isn’t volume. It’s knowing which experience would change the model’s behavior, and proving that it did.
O’WOW is a research-led data intelligence company for Physical AI. We start from the model’s failures, in production and in our own testing, and build the collection, validation, and evaluation loop that turns those failures into the next model version.
About O’WOW
The hard part of Physical AI isn’t storage or compute. It’s deciding what to capture, and then proving it was worth capturing. That decision is where we work.
We start with the model and the product it must power, then connect targeted data collection, hierarchical quality checks, and continuous evaluation to the tasks it needs to perform in the real world.
Our collection priorities come from where models fall short, in production and in rigorous testing. We use those failures to decide what to collect next, and we compare model versions to understand what changes, what improves, and what remains unresolved.
We start with the model’s limitations, not a quota of data hours.
Approach
Data integrity is necessary. It’s not enough. A recording can be technically sound and still miss the task, environment, or failure mode that matters to the model. Our approach connects quality at capture with evaluation at the point of use.
Define the end product.
Establish the tasks, operating conditions, and performance criteria the model must meet.
Investigate failures.
Use production feedback and controlled testing to identify limitations and prioritize collection hypotheses.
Collect against those gaps.
Target relevant behaviors, environments, and variations, with traceable provenance.
Validate in layers.
Check capture integrity, task execution, and fit to the intended training and deployment requirements.
Evaluate model versions.
Compare performance on repeatable tests grounded in the intended operating conditions, tracking improvements and regressions.
Repeat.
Use the remaining failures to inform the next collection and evaluation cycle.
For Model Builders
We deliver high-fidelity, sensor-rich Physical AI data at the scale foundation-model training demands. Every hour arrives verified through the same hierarchical checks and production benchmarking we apply to everything we ship, ensuring multimodal volume for tasks that cannot be learned from pixels alone.
Bring us the model, the tasks it needs to perform, and the limitations holding it back. We’ll work from those requirements to define collection priorities, acceptance criteria, and evaluation protocols, then stay with the problem through the next model version.
- Targeted collection plans tied to specific tasks and failure modes.
- Traceable data and layered validation against agreed requirements.
- Model-version benchmarking grounded in the intended use case.
- Continued evaluation to inform the next round of collection.
Work with us on your model and deployment requirements. Book a call
Research
We treat data collection and evaluation as research problems: which experiences help models generalize, how failures should guide new collection, and how to distinguish a benchmark gain from a meaningful improvement in the real world.
Our ambition is to collaborate with leading robotics researchers across continents, connecting advances in robot learning with the practical demands of collection, testing, and deployment.
- Failure-driven collection and data selection.
- Generalization across tasks, environments, and embodiments.
- Reproducible evaluation and model-version comparison.
- Understanding which data interventions produce useful changes in model behavior.
Team
O’WOW’s founders come from two worlds. One brings research experience from labs like Carnegie Mellon University and Google DeepMind, combined with experience building production-scale systems and processes at Google Search, Amazon, and Google Cloud. The other is a repeat founder with a deep operations background who built and sold a company. Research and operations run as one function.