Data Engine
Capture, in real environments. Data, on delivery.
Nero operates a single continuous system connecting real physical work to the datasets used to train robotics and embodied AI models. Every stage is engineered end to end: Nero controls the hardware, the ingestion pipeline, the annotation process and the delivery format.
Pipeline stages
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01
Capture
Nero-designed capture hardware deployed into real operating businesses, recording first-person work rather than staged demonstrations.
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02
Ingest
Recorded data moves off-device into Nero's pipeline with integrity checks, encryption in transit, sensor synchronization and dataset indexing.
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03
Process
Raw captures are calibrated, split into frames or clips, filtered for quality and redacted where a client's specification requires it.
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04
Enrich
Structured labels are added to the underlying capture: tracking, segmentation, depth, pose, actions, interactions and language annotations, depending on the dataset specification.
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05
Validate
Automated checks and human QA where specified confirm the dataset matches its schema and covers the required task distribution before delivery.
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06
Deliver
The finished dataset is versioned and packaged into the client's schema, delivered via API, object storage or batch export, or streamed continuously under a Continuous Data program.
Why It Matters
One system, not a chain of vendors.
Collection, hardware, processing and annotation are usually separate vendors stitched together by the customer. Nero operates all of it as one pipeline, so a dataset can be built around a client's specification instead of assembled from whatever each vendor already produces.