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Describe any scenario to find matches over sequences of events from synchronized video and telemetry.
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Describe a scenario to find every match over millions of hours of video. Curate more diverse, balanced and high-signal Physical AI datasets.
natively understands temporal sequencing of video and telemetry. We are faster, cheaper and more accurate than frontier models. And unlike naive vector search, it actually works.
Accuracy (F1), all 10 scenarios · 6 shown below
REASSEMBLE (Sliwowski et al., 2025): 149 robot assembly episodes, 13.3 hours of video and telemetry. F1 against human-verified clips. GPT-6 Astra and Gemini 3.8 Flash watched every episode at 1 fps. Six of the ten scenarios are shown; the others are gear pick failure, gear pushed onto shaft, gear push fails to seat, and gear push, realign, push.
robot fails to insert a part, sets it back down, then picks the same part up again for another try
Describe any scenario to find matches over sequences of events from synchronized video and telemetry.
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Watch for it automatically across live and historical data.
An alert within a minute, plus how often it happens.
Sample from your corpus into a dataset with a deliberate mix: diverse, deduplicated, versioned.
Bucket by scenario, robot, gripper force, or time of day.