# Training Video Data — VideoDB > The training-data layer for world models, VLAs, and physical AI. We partner with some of the largest video data providers and frontier labs to take petabyte-scale raw footage and stand up a queryable, training-grade dataset. Custom labeling models, human-in-the-loop verification, provenance per clip. **Status:** Training Video Data --- ## Hero stats - **1M+ videos** — Indexed across partner programs - **PB-scale** — Tuned for training pipelines - **<200ms** — Search across the full corpus CTAs: [Book a consult](/company#contact) · [See the lab](https://labs.videodb.io/) --- ## The bottleneck World models are hungry for video. Most teams are still stuck building pipelines. Before training can begin, raw footage has to be cleaned, clipped, indexed, labeled, and delivered. That work slows teams down long before the model ever sees the data. 1. **Scale.** "Our pipeline cracks every time the corpus doubles." Multi-million-hour archives outrun ad-hoc scripts. Even the biggest labs end up shipping their own curators just to keep the ingest moving. 2. **Specificity.** "Off-the-shelf tags don't speak our taxonomy." Every team wants a different slice: camera motion, contact-rich manipulation, edge cases, locomotion gait. Generic annotators give you generic labels. 3. **Provenance.** "Every clip needs a paper trail." Source, license, capture context, consent: non-negotiable for any defensible training run. Most pipelines bolt this on later. We start with it. 4. **Reuse.** "Today's prepped samples die in S3." Painstaking sample-prep work disappears into a bucket and never gets queried again. The next training run re-does most of it. --- ## Case study — Turning 100,000+ hours of archived footage into training-ready clips **Case study · Video data provider** A large video data provider had a massive archive, but the metadata was only useful at the video level. A model lab didn't want full videos. They needed precise 6- to 10-second clips for training, pulled from hundreds of thousands of hours of footage. The archive already had the raw material. The problem was retrieval. VideoDB processed the footage into scene-level understanding. Existing video-level tags became a starting point, then each scene was indexed with richer context, custom labels, and searchable metadata. The provider could now search across the full archive, find the exact moments a model lab needed, and extract clips instantly. The clips weren't limited to a fixed duration. Teams could retrieve a 6-second moment, a 10-second sequence, or a longer training sample depending on the use case. > "We didn't just unlock old footage. We turned a dark archive into a product model labs can search." What was once a dark archive became a searchable data product. Model labs got the specific video samples they needed for training. The provider got a repeatable way to turn old footage into new revenue. Every new batch added more searchable memory to the archive. **The opportunity.** Most media archives are sitting on the data model labs want. The issue is that the data is trapped inside long videos, coarse tags, and storage systems built for playback. VideoDB turns those archives into scene-level, searchable, clip-ready datasets. --- ## Case study — A query interface over a multi-petabyte training corpus **Case study · Searchable training catalog** ### 01 · Aggregation queries — find how many of a specific clip you have "How many clips do we have with people and a dog, outdoors, no NSFW?" Count and slice the corpus before you plan the training run. The question every dataset planner asks first. ### 02 · Deep search engine — tag filters and natural language, together Compose structured filters (location, safety, audio class, visual class) with free-form scene descriptions. "Outdoor + safe + violin playing + sunset" returns the exact moments, not just the videos that contain them. ### 03 · Flexible clip length — re-clip without re-encoding VideoDB doesn't generate a new mp4 for every clip. The training team can sweep clip lengths (2s, 8s, 16s, episode-level) without re-encoding the corpus. A genuine superpower when you're tuning context windows. ### 04 · Modify samples in pipeline — redact, enhance, resize, transcode in one pass Remove PII (faces, plates, on-screen text), redact restricted content, enhance low-light, resize to the target resolution, transcode to your training format, all in the same pipeline that retrieved the clip. No round-trip to a separate processing job. --- ## For robotics & VLA teams **Real-world video in. Validated robot data out.** VideoDB turns robot streams, sim renders, and camera feeds into searchable context for training and evaluation. Use one layer to inspect rollouts, find edge cases, compare real and synthetic data, and export the exact clips your models need. - **Real-time perception ingest.** Connect RTSP feeds, robot cameras, desktop streams, and sim renders. Index fresh video as it arrives. - **VLA and world-model validation.** Wrap model outputs as indexes. Score rollouts, catch regressions, and surface edge cases. - **Sim2real bridge.** Search real and synthetic episodes through one layer. Export reusable slices for Isaac Sim, Newton, and MuJoCo. --- ## Our approach **We embed. Your data prep gets fast. Your samples stay queryable forever.** A research-grade partnership, not a vendor relationship. We've built this twice. We know the failure modes. 1. **Phase 01 · Audit.** We sit with your team for a week. Map your taxonomy, your storage layout, your eval needs, your gaps. Leave with a concrete pipeline brief. 2. **Phase 02 · Build.** Custom labeling models. Indexes wired to your taxonomy. Human-in-the-loop for the long tail. Provenance and license trail attached to every clip. Immutable. 3. **Phase 03 · Hand off.** A searchable, versioned dataset on your infra, reusable across training runs. The same indexes scale to every new batch you ingest. No more raw-bucket dead weight. --- ## Under the hood — 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 & dedup.** Per-clip quality, near-duplicate detection. Train on what's worth training on. - **Scene & event segmentation.** Reproducible scene and event boundaries you can slice the corpus against. - **Custom labeling models.** Bring your taxonomy. Your labeling model wraps cleanly as an index. - **Provenance trail.** Source, license, capture context attached to every clip. Immutable. - **Lab-grade reproducibility.** Versioned slices, run logs, deterministic exports. Auditable training runs. --- ## Built in our own lab **Validated on real video workloads.** VideoDB comes from our own work in multimodal retrieval, evaluation, and video data preparation. When we work with you, we bring patterns already tested on large archives, model datasets, and production workflows. - **Research note — Evaluate VLMs on your own video data.** A practical playbook for benchmarking vision-language models against your corpus. [Read the post →](https://labs.videodb.io/research) - **Inside the lab — What we're building now.** Open notes on retrieval, eval design, sample efficiency, and video-language alignment. [Visit the lab →](https://labs.videodb.io/) --- ## Closing Bring your corpus. Ship a training-grade dataset in days. Some of the largest video data providers run on this pipeline. CTAs: [Book a consultation](/company#contact) · [See the platform](/platform) --- © 2026 VideoDB, Inc. · videodb.io · hello@videodb.io