How the AI Findability assessment works.

We score first, then move the needle.

We always score before we touch anything, then re-score every month so you can show the number moving. The score is deterministic, so the same inputs always produce the same score. We read each channel through the YouTube API, read everything an AI would, and cross-check that read across multiple leading models so the score is not one model guessing.

Why we measure the text, not the footage.

An AI engine cannot watch your video. Picture your videos as the artwork on the walls of a gallery, but AI walks in blind. You have to put up a placard that names each piece and describes 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.

We triage by citation potential, not views.

Not every video earns the same effort. Short promos and teasers rarely answer a question; longer instructional 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. 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 content dense with facts, statistics, and named sources lifts citation by roughly 30 to 40 percent. Transcripts matter for accuracy: 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. AI Overview answers change often with no correlation to search volume, and a large share of the domains cited for a question differ a month later. Propagation takes days to weeks, 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.