Turn video
into text, tone, and detail.

A different approach to AI video description.

AI video description is the process of using computer vision, speech recognition, and natural language processing to turn raw video into text: what’s shown, what’s said, and what it means. Marketers, researchers, and production teams have used it for years to make raw footage searchable, to catch details a human reviewer would need hours to find, and to turn stacks of video into decisions.

Why watching video by hand breaks down

For most teams, video review means someone scrubbing through a timeline with a notepad open. A researcher rewatches a session to catch one quote. A marketer scrolls through raw footage looking for the clip with the right energy. The tools that transcribe video stop at the words: a caption track with no sense of what is on screen, how the speaker sounded, or which thirty seconds actually matter.

Reading the video, not just the captions

Speak AI treats every video the way a producer would, at machine speed. Each file is transcribed in your language, with 100+ supported, and then the video itself is read: on-screen text, scene changes, tone and energy, and the pacing of what is being said. Names, brand mentions, timestamps, and outcomes are extracted into structured fields your systems can use.

Then the questions start. Ask across your entire video library with AI chat, using the same high-quality prompt workflows teams once stitched together manually, now running natively over your footage with ChatGPT, Claude, and Gemini built in.

What teams ask their video library

• “What’s happening in this video, scene by scene?”
• “Which clips mention our product or brand by name?”
• “Show me every video where the speaker sounds frustrated or excited.”
• “Summarize the key moments across all interviews this month.”
• “Which raw footage has usable clips for social, and how long are they?”

From raw footage to a searchable library

The result is a video library that describes itself. Scenes, speakers, and moments are tagged automatically. Brand mentions and key quotes surface without a rewatch. Trends across hundreds of files become a report instead of a hunch, and dashboards you can customize and white-label track themes and sentiment over time, so this month’s footage is measured against last month’s. A respected media brand put its raw conference video through this workflow and turned 500+ hours of footage into high-performing content, without adding headcount.

And because video rarely lives alone, the same engine scores calls, meetings, and recordings on the same criteria, connecting your video library to call scoring and the MCP server, so any assistant can query it.