Video platform for AI workloads.

Cloud infrastructure for teams that need playback, processing, and rendering for AI video applications at scale.

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Video workloads aren't always predictable.

Large video datasets and sudden spikes create an infrastructure problem: queues, retries, media preparation, and orchestration. Editframe handles the video workflow so your team can focus on its models and product.

2 million hoursof recordings to prepare, analyze, and turn into useful results

  1. Connect media
  2. Parallel processing
  3. Data + clips
Illustrative workload, not a benchmark. Connect a recording library, run processing in parallel, and use the results as data or clips.
  • Large volumes

    Work through recording libraries and video datasets.

  • Bursty demand

    Handle batches and changes in demand without building your own peak-capacity stack.

  • Managed execution

    Leave dependencies, run tracking, and retries to the platform instead of custom glue code.

Built for workloads like

  • Robotics & embodied AI
  • Egocentric data pipelines
  • Video intelligence
  • Highlights at scale

Build the video features you need.

Start with ingestion, playback, pipelines, or rendering. Use one capability or connect them into a complete workflow.

  1. Ingestion

    Bring video in

    Accept resumable uploads or read video from your existing storage.

    Explore Ingestion

  2. Playback

    Play and inspect

    Build video experiences with player components or HLS/DASH delivery.

    Explore Playback

  3. Pipelines

    Process video

    Prepare recordings, connect AI models through your analysis services, and run independent processing steps in parallel.

    Explore Factory

  4. Rendering

    Render outputs

    Create clips, highlights, and personalized video using HTML, CSS, and React.

    Explore Rendering

Define a pipeline in one file.

Combine built-in video processing with your own analysis services. Factory manages dependencies, tracking, and retries so you don't have to build orchestration around every model.

Explore pipeline reference

pipeline.json

{
  "steps": {
    "transcript": { "type": "transcribe" },
    "frames": { "type": "sample_frames" },
    "analysis": {
      "type": "endpoint",
      "needs": ["transcript", "frames"],
      "with": {
        "endpoint_id":
          "00000000-0000-0000-0000-000000000000"
      }
    }
  }
}
Illustrative definition. Replace the endpoint ID with your registered HTTPS analysis service; your service supplies the model integration and returns Factory annotations.

  • Parallel steps

    Run independent processing steps together.

  • Recovery

    Inspect attempts and retry failed work.

  • Your models

    Connect analysis through a registered HTTPS endpoint.

From recordings to useful results.

Process a recording library with your own models, then use the results to produce datasets, structured insights, or highlights.

  1. Connect

    Video in your storage or uploaded files.

  2. Prepare

    Transcripts, frames, and scenes.

  3. Analyze

    Your models produce timed findings.

  4. Process

    Filter, segment, or select the footage you need.

  5. Output

    Structured data, prepared assets, or clips.

  • Model-ready datasets
  • Structured metadata
  • Clips & highlights
Illustrative end-to-end integration, not a throughput benchmark. Factory orchestrates built-in steps and your analysis endpoints. Dataset curation depends on your workflow; your application can use Elements to render selected clips.

Video infrastructure you don't have to build from scratch.

Uploads, playback, processing pipelines, and rendering work together without requiring a new stack of queues, scripts, and services for every workload.

  • Recover interrupted uploads

    Resume streamed uploads and inspect readiness before later processing begins.

  • Reuse your compositions

    Design a composition once, then supply different media and data to generate variations.

  • Turn findings into outputs

    Use timed results for clips, redactions, annotations, or publishing workflows.

  • Track work to completion

    Inspect runs, retry failed pipeline work, and receive signed webhooks for job outcomes.

What are you building with video?

Whether you're preparing AI training data or building video experiences, tell us about your volume, your workflow, and what you need to ship.

Talk to usRead the docs