# World Model Training Data — VideoDB

> Training data infrastructure for the physical AI era. World models, robotics, and autonomy don't need another upload tool. They need structured video at scale — with provenance.

**Status:** World Model Data · Program in market · Partner-first

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## Hero stats

- **PB-scale** — Multi-million-hour corpora across cloud and partner sources
- **Lab-grade** — Reproducibility, deduplication, quality scoring per clip
- **Provenance** — Source, license, capture context — every clip carries the trail

CTAs: [Talk to us](/company#contact) · [See the platform](/platform)

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## The problem

Getting from raw footage to training-ready data shouldn't take a quarter. World model and physical AI pipelines need scale, structure, and provenance — and most teams build it by hand.

1. **Scale problem.** Multi-million-hour corpora crack internal pipelines.
2. **Structure problem.** Models need scenes, events, quality tiers. Upload tools don't do that.
3. **Provenance problem.** Source, license, capture context — every clip needs a paper trail.

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## Built for

Teams training models on the physical world. Partnership-first. Data infrastructure partner, not a competing model lab.

- **World model labs** — Curated, structured video at the scale and quality model training requires.
- **Robotics & autonomy** — Filter for the events, scenes, and edge cases that matter to the policy.
- **Simulation** — Ground simulation against real-world video — searchable, structured.
- **Video data providers** — Productize raw footage as queryable, licensable datasets.
- **Research consortia** — Multi-party datasets with consistent structure, access controls, provenance.
- **Internal data platforms** — Replace bespoke labeling and curation with one platform.

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## Capabilities — from raw footage to training-ready data

The pipeline the modeling team would otherwise build by hand — standardised, reproducible, audited.

- **Petabyte ingest.** Files, datasets, RTSP captures — throughput tuned for corpus-scale pipelines.
- **Quality scoring.** Per-clip quality, dedup, near-duplicate detection. Train on what's worth training on.
- **Scene segmentation.** Reproducible scene + event boundaries you can slice the corpus against.
- **Event labeling.** Bring your taxonomy. Indexes as code — your labeling model wraps cleanly.
- **Provenance trail.** Source, license, capture context attached to every clip — immutable.
- **Lab-grade reproducibility.** Versioned slices, run logs, deterministic exports. Auditable training runs.

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## The shortest path

Bring your corpus. Get a queryable, training-ready dataset. The data infra you would otherwise build — configured for your scenes, events, and provenance schema.

```bash
videodb dataset create --schema robotics.yml --source s3://your-corpus/
```

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## Two tracks for the world-model wedge

### Research track

Co-build the training pipeline for a frontier world model.

For lab teams · physical AI · robotics · autonomy.

We embed an engineer in your team. Your model wraps as an index. Reproducible slices, audit logs, deterministic exports. [Talk to us →](/company#contact)

### Partner track

Productize your corpus as a queryable, licensable dataset.

For data providers · sovereign clouds · research consortia.

VideoDB sits on your hosting as the structured-video layer. Sovereign cloud partnership in market. [Read the brief →](/company#partners)

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## Closing

Stop building data infrastructure. Start training models. Partner-first. Lab-grade reproducibility. Provenance per clip.

CTAs: [Talk to us](/company#contact) · [See the platform](/platform)

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