❓ Problem

For a YouTuber, an entire career rests on one number: retention. Get the first 30 seconds wrong and the video is dead on arrival, because YouTube's algorithm reads an early exit as a weak content match and quietly stops recommending it. The data is brutal. If the intro does not land, over a third of viewers are gone inside 30 seconds, more than half have left by the one-minute mark, and only about 16% of people ever reach the final 10 seconds.

Here is the cruel part: the timing. YouTube hands every creator a gorgeous 100-point retention graph showing exactly where the audience bailed, the cliff in the intro, the dip at 0:12, the slow bleed through the middle. But it only appears after you publish, when there is nothing left to fix. Creators pour days into an edit, hit upload, and find out whether it holds attention at the precise moment they can no longer do anything about it.

So every publish is a high-stakes guess, repeated weekly, by millions of people whose income depends on getting it right. There are 115 million YouTube channels and roughly 3 million monetised under the Partner Program, and YouTube has paid creators around $70 billion in just three years. The single biggest lever on all of it, retention, is the one thing you cannot see until it is too late.

✅ Solution

A pre-publish retention predictor that scores any unpublished video and draws its drop-off curve before you ever hit upload.

• Connect your channel and the model learns you. It trains on your own real retention history, every video you have ever published, blended with a pooled set of thousands of other videos in your niche. Your prediction is grounded in how your specific audience actually behaves.
• It watches the draft, frame by frame. A multimodal model ingests the actual cut, the pacing, the audio energy, what is on screen, and forecasts where viewers will leave and why. You get the same 100-point curve YouTube would show you, except now it is a prediction you hold before publishing.
• The feedback is specific and timestamped. Not "improve your intro" but "move the payoff to 0:04", "cut the dead four seconds at 0:12", "add a pattern interrupt at 1:45". Edits tied to the exact second they matter.
• Every publish sharpens the next call. When the video goes live, its real retention curve feeds back in, the model checks its own prediction against reality, and the next forecast gets tighter.

The wedge is one screen: the predicted curve on an unpublished draft. The platform underneath, a model trained on millions of draft-to-reality curve pairs, becomes something no incumbent, and arguably not even YouTube, currently bothers to build.

📊 Key Numbers

Market size

• The creator economy was worth roughly $250 billion in 2025 and is projected to pass $1 trillion in the early 2030s, growing at about 23% a year. Video is the single largest slice at around 39% of revenue.
• There are over 200 million creators worldwide. YouTube alone has 115 million channels, of which about 3 million are monetised and only ~4.3% qualify for the Partner Program at all.
• YouTube did over $60 billion in revenue in 2025, with roughly $22 billion available for creator ad payouts in a single year. This is the pool creators are fighting over, and retention is how they win their share.

ARR potential

• Bottom-up: 100,000 creators on a $39/mo plan is roughly $47M in subscription ARR. The serious-creator SaaS market proves the willingness to pay, vidIQ says over 20 million creators have used it and TubeBuddy has run studies across 59,000+ of its users.
• A retention-specific tool can charge more than a generic SEO add-on, because it moves the one metric tied most directly to income. Price against revenue saved, not feature count.
• Realistic path to $20-40M ARR as a focused creator product, with a far higher ceiling if it becomes the default pre-publish check or licenses the underlying prediction model to editing suites, agencies and media companies.