Video Generative Engine Optimization

Make your videos
findable by AI.

AI can't watch a video. It reads the text around it. We make that layer legible and citable, so ChatGPT, Gemini, and Google's AI find your video and cite it as the answer.

Step 1 Score your Video for AI Findability
Step 2 Improve your score
The opportunity

You're sitting on a goldmine that AI can't read yet.

Every video you have ever published is a potential answer an engine could cite. But AI cannot watch a frame of it. It reads the title, the description, the transcript, the chapters, and the page the video lives on. Make that text layer readable and the library you already own becomes a source AI quotes, with no new production required.

Why video wins

YouTube is the number one source AI cites.

Across independent analyses from BrightEdge, Profound, and Semrush, YouTube is the most-cited domain in Google's AI Overviews, and it sits near the top across ChatGPT, Gemini, and Perplexity. Video is not a nice-to-have for AI visibility, it is the single biggest opening you have. And most of those citations go to long-form video, the exact content brands already own and leave unreadable.

#1

most-cited domain in Google's AI Overviews (BrightEdge, Profound, Semrush).

94%

of YouTube citations in AI answers come from long-form video.

Top

ranked source across ChatGPT, Gemini, and Perplexity, not just Google.

What gets cited

AI cites useful videos, not popular ones.

Views, likes, and subscribers barely move whether an engine quotes you. AI picks the clearest, most credible answer to the question, not the most-watched clip. A small, tightly focused library can out-cite one with millions of views, as long as the answer is readable and trustworthy.

Useful · answers a real question Legible · a readable text layer Credible · corroborated where it counts
How AI finds video

It reads, it never watches.

When someone asks an AI a question, the engine re-crawls the web, pulls the most legible and credible passages it can read, and rewrites them into an answer with citations. The text and structure around your video decide whether you are in that answer. Not the production value, not the view count.

What it reads
TitleDescriptionTranscript ChaptersSchemaHost page

The whole text layer around the video. If it is thin, you are invisible.

How we make your video findable

Three waves, from readable to cited.

Wave 1

Fix the metadata

Titles rewritten as buyer questions, answer-first descriptions, corrected transcripts, chapters, tags, and links, on the videos you already have. No new production.

Wave 2

Own the citation

Watch pages on your own site with the transcript as readable text plus VideoObject and FAQ schema, so the citation credit lands on your domain.

Wave 3

Compound it

Syndication, seeding, and new content that answers the questions no one else has claimed, so your findability builds over time.

We measure it, so we can move it

Where findability is won and lost.

Your readiness score
0–100

One score, built from three layers. Here is what each contributes out of 100:

15
60
25
Channel signals 15%Video text layer 60%On-site + off-platform 25%

The 0 to 100 score shows exactly where your videos stand. The video text layer is 60% of it, the biggest lever. Once you have your score, we help you improve it so you show up in AI search.

See how the assessment works →

Get your AI Findability score.

See exactly where your videos stand, then we help you improve it so you show up in AI search.

Score your Video for AI Findability →
The method

How the AI Findability assessment works.

We score how ready your videos are to be found and cited by AI, from 0 to 100, then we move the number. Here is how the assessment reads your channel, what it measures, and how the work is prioritized.

We score first, then move the needle.

We always score before we touch anything, then re-score every month so you can show leadership the number moving. The AI Findability score is 0 to 100 across three layers, and it is deterministic, so the same inputs always produce the same score. We built the assessment as a set of skills that pull each channel through the YouTube API and read everything an AI would, then cross-validate that read across multiple leading models (Claude, ChatGPT, Gemini, and Perplexity), so we are confident the score is not one model hallucinating.

15
60
25
Channel signals 15%Video text layer 60%On-site + off-platform 25%

Channel and entity signals, the per-video text layer, and the on-site plus off-platform layer. The video text layer is the biggest lever, at 60 of 100.

Why we measure the text, not the footage.

An AI engine cannot watch your video. Picture your videos as the best artwork on the walls of a beautiful gallery, but AI walks in blind. You have to put up a placard that describes each piece, its name and what is in it, so the engine can understand and cite what it cannot see. That placard is the text and structure layer: the title, description, transcript, chapters, tags, and the schema on the page that hosts the video. Without it, the video is invisible to AI no matter how good the footage is.

How we read your channel.

The assessment pulls each channel through the YouTube API and reads the mechanical fields an engine relies on, the length, the chapters, the descriptions, and the tags, alongside the channel-level signals and, where it is reachable, your website. Mechanical fields are detected automatically; the judgment fields, like whether a title is a real question or a description is answer-first, are read against one uniform, disclosed rubric so every channel is scored the same way and benchmarks are fair.

We triage by citation potential, not views.

Not every video earns the same effort. Short promos, teasers, and launch clips rarely answer a question; longer instructional, explainer, and how-to content on evergreen topics is rich with citable answers. We score the whole catalog by citation potential and rank it into tiers, Tier 1 being the highest leverage. It is battlefield triage: you pick the battles where effort changes the outcome. Views come back only as a secondary lens, so a Tier 1 video that is also well watched is the fastest visible win and goes first.

What actually makes a video citable.

Legibility plus credibility. A few things move the needle most: long-form video is about 94% of all YouTube citations in AI answers; a substantive, answer-first description is the single strongest positive signal measured; chapters multiply the citation surface, since about 73% of cited videos are timestamped and 78% of those are cited across more than one chapter; and content dense with facts, statistics, and named sources lifts citation by 30 to 40 percent. Transcripts matter for accuracy, not file format: auto-captions garble the exact proper names, products, and technical terms people search, so a corrected transcript is what counts.

AI re-checks constantly, so retrofitting works.

The common objection is that AI already saw your video and will not look again. That describes an old search index, not how AI answers work. Modern answers are generated at query time by re-crawling the live web, so updated metadata gets re-evaluated on the next pass. The same AI Overview answer changes roughly every 2.15 days with no correlation to search volume, 40 to 60% of the domains cited for a question differ a month later (70 to 90% over six months), and AI answers cite content about 26% fresher than classic search. Propagation takes days to weeks, not seconds, and retrofitting beats re-uploading, which throws away the video's age, engagement, and earned citations.

Then we move the needle in three waves.

Wave 1 fixes the text on YouTube itself: question-style titles, answer-first descriptions, corrected transcripts, chapters where they earn their place, and tags. Wave 2 fixes it where the video lives on your own site: a watch page with the transcript as readable text and VideoObject and FAQ schema, so the citation credit lands on your domain. Wave 3 builds credibility beyond readable, corroborating the same answer across places you own and places that reference you, and creating content for the white space no competitor owns yet.

Sources: Views vs. citation, Otterly (100M+ AI queries); AI Overview change rate and RAG freshness, Ahrefs; citation drift, Profound; fact-dense citation lift, Princeton.
Freshness
~2 days

how often AI answers change, so updated videos get re-evaluated fast.

Density wins
30–40%

more citations for content rich with facts, statistics, and named sources.

Time to show up
Days

to weeks for updated metadata to start appearing in AI answers.

Answers

The questions marketers ask about video and AI.

Browse the full answer library →

Step 1

Score your Video for AI Findability.

Tell us your channel and we run your AI Findability audit. You get your 0 to 100 score and the top gaps holding your videos back from AI search.

Then Step 2: book a call and we help you improve it.

How the assessment works →

The assessment also scores how readable your video embeds are on your website.

Free. We email your score, usually within a day.

Step 2

Improve your score.

Once you have your score, we move it. See exactly how we take your videos from invisible to cited, wave by wave, then book a call to learn more.

See how we improve your score →