Skip to content
Walkstamp

Use case

A video the AI can actually read end to end

A model watching video samples one frame per second, blind. An hour becomes 3,600 images, over a million tokens, and most of them are the same static screen. The document solves that before it ever reaches the model.

A page of the generated document, with the brand on top, the identification, and each screen next to what was said12
How the page comes out. What appears changes with the scenario: test evidence carries wall-clock time and a checksum; a work instruction carries neither.

Three things before you send it to the AI

A long video becomes several subjects. Mark where each subject starts and walk away with one document per subject — each fits in one conversation, instead of blowing the context window all at once.

Your domain’s vocabulary, before you send it. System names, acronyms and products the transcript gets wrong are fixed in one go, and the AI reads the right text.

Redact before pasting. What cannot leave the company is covered in the image, not hidden: the redaction is burned in, and what you send has no data underneath.

Prepare a video for the AI free, and the video does not leave your computer

The pain

You have a recording — a demo, a lesson, a support session, an incident — and you want to ask a model things about it. Sending the file hits three walls: the size limit, the cost of processing repeated frames, and the fact that most models simply do not accept video.

And even where they do, the model loses what was said alongside what was shown: the speech arrives as a loose block of text, with no idea which screen it belongs to.

What comes out

A PDF (or Markdown, HTML, JSON) any model can read, with the information already organised:

  • ~60 screens instead of 3,600, because the rest were repetitions of the same image
  • Each image paired with the speech of that stretch, not the whole transcript dumped at the end
  • The exact timestamp of every frame, so the model can cite "at 04:12" precisely
  • It fits any model, including the ones that refuse video — it is a document, not a media file
  • A ready-made prompt, written so the model understands what it is reading before it answers
  • Structured JSON when the destination is not a chat but your own code or your agent

How to do it

  1. Leave the use case on Context for AI — it is the default.
  2. Drag in the video, or record the screen on the spot.
  3. Let it transcribe and pull the frames. Discard what does not matter in the review: every screen fewer is a token fewer.
  4. Produce the PDF, copy the prompt, and take both to the AI you use.

What it does not do — better to know up front

It sends nothing to any model. It assembles the material and hands it to you. You choose the model, the account and the usage policy — including a model running inside your own company.

It does not replace watching the video when movement is the data. If what matters is an animation, a transition or someone’s gesture, scene-change frames lose that. For system screens, which is the common case, they catch everything that matters.

It does not summarise by itself. The summary is the model’s job; ours is to make the model read the right thing.

Prepare a video for the AI See the plans

It is the most general of the five, and the tool’s default — when you do not know which document you want, this is the one that works.