What AutoClip does
Connect your YouTube channel and AutoClip analyzes your own video and analytics history
to build a working model of what your audience actually responds to — your channel's
"DNA." Every clip candidate it finds is scored against that model, not a one-size-fits-all
score borrowed from someone else's channel.
- Setup, not aftermath. Clips are cut to start at the setup of a moment, not the punchline — so the context survives the cut.
- Named reasoning. Every score cites a specific, named principle, so the "why" behind a ranking is never a black box.
- Your channel's own signal. Ranking is grounded in your DNA and, once you've given enough feedback, a preference model trained on your own upvotes and downvotes — never a generic benchmark.
Proof of lift: we measure what actually happened
Most AI clipping tools stop at generating the clip. AutoClip closes the loop: once you
publish a clip through AutoClip, it tracks how that clip actually performed — views at
the 48-hour and 7-day checkpoint, compared against the median of your own comparable
published Shorts, not an industry benchmark. That outcome feeds back into how future
clips are ranked for your channel specifically.
This is reported honestly: a clip needs enough comparable history before AutoClip will
call a verdict, and a rate is only shown once there's enough judged data to state one
with a confidence interval. Raw counts are shown before that threshold — never an
invented trend.
See yours →
How we measure clip quality — and what the number means
Most tools in this category ship a single score claiming to predict how well a clip
will do. Their own users describe those scores as decorative. We don’t have one,
because we can’t honestly produce one. What we can do is show our work on the part
that is actually testable: whether the engine cuts the clip in the right place.
AutoClip’s clip engine runs against 35 adversarial evaluation scenarios
on every change to the engine, and all 35 pass. Each scenario is a
hand-built timeline designed to break a naive “clip the loudest moment” approach —
for example aftermath_louder_than_setup, where every loud signal is stacked
after the payoff and the engine must still anchor the clip to the quiet setup
beforehand. Others cover false peaks, cold opens with no silence lead-in, interrupted
setups, dead air, overlapping peaks, and missing transcripts.
What this number does not mean. These are synthetic fixtures, not real
videos, and passing them does not predict how any clip will perform. It is evidence of
one specific thing: that the engine clips the setup rather than the aftermath, under
conditions built to make it fail. We publish it because it is checkable — the scenarios
live in the codebase as readable files, so the claim can be verified rather than trusted.
We deliberately do not publish aggregate ranking-quality statistics pooled across
creators. YouTube’s API Services Developer Policies restrict aggregating data across
channels that aren’t under a common content owner, and require that any permitted
aggregate stay visible only to that owner. Your channel’s numbers are yours; they are
shown to you inside the product and are never pooled into a public figure.
Pricing
No subscription, no expiry. Buy minutes once, use them whenever.
Stream
10,000 min
$400.00
Minutes cover source-video processing time (transcription + AI analysis + render); one
minute of source video is one minute deducted. Full details and a live balance at
/app/pricing.