# Products · VideoDB

> Products built for teams processing massive amounts of video.

Scalable infrastructure and the expertise to go with it, so your team builds intelligence from video faster: from training the model to testing it in the world.

Canonical: https://videodb.io/products

## 01 / Video primitives: the video infrastructure underneath everything

Transcoding, frame tiling, real-time ingestion and high-throughput pipelines, built in house. Ask for one frame or one window and get exactly that, with no cutting and no re-encoding.

- **In-house transcoding**: every format in, one addressable form out. Built for random access, not playback.
- **Frame tiling**: a whole window in one image, at the resolution the question needs.
- **Frame-addressable streaming engine**: any window of any episode, addressed by frame, streamed to a model or a person in one request.
- **Real-time ingestion**: cameras, streams and screens enter the same pipeline as an archive.
- **High-throughput pipelines**: 100,000 hours turned into scene-level samples in two weeks, files left in place.

The architecture: https://videodb.io/platform.md · Docs: https://docs.videodb.io

## 02 / Machine annotation: dense annotation of every hour you have, at archive scale

Connect your data sources and data providers. Run any model as an analyzer: segmentation, dense captions, events, the fields you define. 100K+ hours in weeks, with human QC through our partners.

- **Any model as an analyzer**: ours, open-weight, proprietary, or the one you trained last week. One versioned index per analyzer; a better model re-reads the archive.
- **Segmentation and dense annotation**: objects, hands, activities, on-screen text, speech, outcomes: per frame, per scene, per episode.
- **Data sources and data providers**: your buckets, your fleet, and the collection partners who record for you, landing in one index.
- **Human QC, with partners**: low-confidence and rare events go to reviewers with the clip. The verdict lands in the same index.

Annotation benchmark: https://videodb.io/blog/wgo-bench-action-annotations.md · Talk about your archive: https://videodb.io/contact.md

## 03 / Episode retrieval: ask your training runs anything

World-leading visual search over every episode you have. Moments and frames in 500 ms. Build agents with deep search on top.

- **Leads the chart on benchmarks**: supports queries that no one else can. Hand your agent the best tool to probe interesting scenarios. Paper: https://arxiv.org/abs/2608.08075
- **500 ms to the moment**: ask in plain English. The window is decided by the question: a slip, a regrasp, a whole shift.
- **From hits to training set**: filter, balance, export a reproducible manifest. Train the next model on it.
- **Agents with deep search**: deep search, aggregation, detail and watch agents, from Claude Code, Cursor, MCP or the SDKs.

How every run improves the next: https://videodb.io/blog/robot-runs.md · See it on your runs: https://videodb.io/contact.md

## 04 / Realtime ingestion: connect 1000s of cameras. Analyze in real time.

Set up alerts and reactive systems. Memory for agents and robots.

- **Search while it streams**: cameras, live streams and screen recordings, indexed while they run.
- **Alerts with the clip attached**: events described in plain language. Every alert arrives with the moment.
- **The robot's memory**: long-term memory is the index. Short-term memory is the evidence stream.
- **Every agent run is evidence**: computer-use agents recorded all the time. Ask what the agent clicked before the error.

From a camera to events: https://videodb.io/blog/rtsp-ai-analysis.md · Ask for a demo: https://videodb.io/contact.md

## 05 / Connected to your robot data: MCAP, LeRobot and RLDS in. Manifests and loaders out.

Your files stay the source of truth for what the robot did. VideoDB takes the video part, the part nobody could search.

- **In**: MCAP, LeRobot, RLDS, RTSP, and files in S3, GCS or Azure. Simulator and world-model output too.
- **Out**: Parquet manifests, a PyTorch loader adapter, MCAP shards, evidence streams, scheduled agent results.
- **Keeps the links**: every result carries episode id, timestamp, camera, source type and model version.
- **Told apart**: real, simulated and generated video stay distinguished, in the index and in every export.

See it on your recordings: https://videodb.io/contact.md