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.
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.
Multi-million-hour archives outrun ad-hoc scripts. Even the biggest labs end up shipping their own curators just to keep the ingest moving.
Every team wants a different slice: camera motion, contact-rich manipulation, edge cases, locomotion gait. Generic annotators give you generic labels.
Source, license, capture context, consent: non-negotiable for any defensible training run. Most pipelines bolt this on later. We start with it.
Painstaking sample-prep work disappears into a bucket and never gets queried again. The next training run re-does most of it.
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. Your footage becomes something customers can ask for, search through, verify, and receive as training-ready video.
"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.
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.
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.
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.
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.
Connect RTSP feeds, robot cameras, desktop streams, and sim renders. Index fresh video as it arrives.
Wrap model outputs as indexes. Score rollouts, catch regressions, and surface edge cases.
Search real and synthetic episodes through one layer. Export reusable slices for Isaac Sim, Newton, and MuJoCo.
A research-grade partnership, not a vendor relationship. We've built this twice. We know the failure modes.
The pipeline the modeling team would otherwise build by hand: standardised, reproducible, audited.
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.
A practical playbook for benchmarking vision-language models against your corpus.
Open notes on retrieval, eval design, sample efficiency, and video-language alignment.
Some of the largest video data providers run on this pipeline.
