A new model
every morning.
Find the clips your model needs in plain English. Stream them to your GPUs at full speed. Cut the data loop for Physical AI from a week to a single day.
One iteration a week
Most of that week goes to data work — finding the right clips, curating them, getting them to your GPUs. Not modeling.
You can’t search inside your video
The clips your model needs are buried somewhere in your footage. Finding them takes days — heuristic scripts, annotation vendors, or manual review. And every new question repeats this cycle.
Your GPUs are starving
You lose 20–40% of training time to data loading. Compute scales 3× per GPU generation, storage 1.3×. Solve the loop and your GPUs train at the speed you bought them for.
Find the clips your model is missing. Feed them to your GPUs at full speed.
Search your video like text.
- →Ask for “left-arm grasp failures on deformable objects.” Get the clips back, curated, in minutes.
- →A new research question is a query, not a 3–5 day annotation cycle.
- →We run the VLMs. You don’t stand up a model-serving stack.
Feed your GPUs to max MFU.
- →Video-native PyTorch DataLoader.
dict[str, Tensor]arrives on-GPU, already decoded. - →7–22× faster than hand-rolled loaders. Saturates B200s today, Vera Rubin tomorrow.
- →Reclaim 20–40% of training time lost to data loading.
Your data, in one place.
- →Video and sensors on the same row, timestamp-aligned
- →Stored in your own buckets, in open formats, and take it with you anytime
- →Dataset versioning and row-level provenance
- →Add columns like embeddings or labels without rewriting existing data
See MultiBase on your data.
Bring us your hardest physical AI dataset. We’ll run MultiBase on it and show you what changes.
Or email multibase@eventual.ai directly.
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