ChatGPT answers, unedited

Yes. There are several platforms that can do long-video → transcript → short-clip suggestions, but there’s an important distinction:

Most tools predict which moments might perform well; fewer actually use your audience’s historical retention data to decide what to clip.

Platforms worth looking at

• opus.pro — probably the closest match to what you're describing. It analyzes long-form videos, identifies highlight moments, generates short clips, adds captions/reframing, and provides a "virality" style score. It supports long videos from sources such as YouTube, Google Drive, and Zoom. Opus Opus
• descript.com — particularly good if you want the transcript itself to be the editing interface. You can search/read the transcript and turn sections into clips, but it's less focused on automatically predicting the best short-form moments. Accelrio
• riverside.fm — good if you're recording podcasts/interviews there already. It combines recording, transcription, editing and short-form clipping.
• vizard.ai — another long-to-short AI workflow, particularly oriented around social clips.

The interesting part: using actual retention data

If by "based on audience retention" you mean:

"Here's my 90-minute YouTube video. Look at where viewers actually stayed, dropped off, rewatched, or engaged, and then recommend 30–60 second clips from the portions that demonstrate strong audience interest."

That's a more sophisticated workflow.

You could combine YouTube Analytics retention data + transcript timestamps + an AI clipping engine. The resulting system could, for example:

1. Transcribe a 60–120 minute video.
2. Break the transcript into candidate moments.
3. Pull the video's audience-retention curve.
4. Identify sections with unusually strong retention / rewatch behavior.
5. Match those timestamps against the transcript.
6. Have an LLM evaluate the surrounding context for a self-contained hook/story.
7. Generate 5–20 candidate Shorts/Reels.
8. Score each candidate using both actual historical audience behavior and content characteristics.
9. Feed the performance of the resulting Shorts back into the system.

That's considerably more interesting than simply asking an AI, "What parts of this podcast sound viral?"

If you're thinking about building a product like this, there's actually a pretty compelling product gap here: "AI clipping based on your audience's demonstrated behavior, rather than generic viral-content heuristics." OpusClip, for example, describes its selection system as analyzing content against social/marketing trends and identifying highlights, rather than specifically optimizing selection against an individual creator's historical retention curve. Opus

If you tell me whether you're looking to buy a tool or build one, I can map out the best options/architecture.

Yes. There are several platforms that do most of this automatically, but there’s an important distinction: most AI clippers identify likely high-performing moments based on the content itself; fewer actually use your channel’s historical audience-retention data to decide what to clip.

Platforms worth looking at

• opus.pro — probably the closest match to what you're describing. Upload a long video and it transcribes/analyzes it, identifies highlight moments, creates Shorts/Reels/TikToks, and scores clips for predicted virality. It also has analytics for published clips. Opus Opus
• vizard.ai — particularly good for podcasts, interviews, webinars, and other speech-heavy videos. It transcribes the video and lets its AI identify/edit sections into clips directly from the transcript. Vizard Vizard Help Center
• descript.com — stronger if you want transcript-based editing and control rather than purely automated clipping. It generates transcripts and can use AI to identify/repurpose sections. Descript
• clipship.co — interesting if privacy matters: it runs locally on your computer, transcribes/analyzes the video, and suggests clips without uploading the footage to a cloud service. ClipShip

But if you mean actual audience-retention data

There's a more sophisticated workflow:

Long video → transcript → retention curve → identify drop-offs/spikes → find surrounding segments → generate clips → compare clip performance → feed results back into selection.

That's different from simply asking an AI, "What are the most interesting moments?"

For example, if your YouTube analytics show that viewers consistently stay engaged around minute 23–26, a system could specifically inspect that section and generate several 20–60 second clips from it. Conversely, if viewers consistently leave during a particular type of segment, the system could learn not to recommend similar clips.

OpusClip is moving in this direction—it says its research system identifies high-retention openings and its platform includes performance analytics—but its public documentation doesn't establish that it takes your individual historical retention curves and uses them directly to select clips. Opus Opus

So if you're asking because you're thinking about building something like this, there's actually a potentially meaningful gap:

An AI clipping platform that doesn't just predict what might go viral, but learns from a creator's actual audience-retention history to determine which moments are most likely to work as snackable clips.

That would be considerably more personalized than the typical "upload podcast → AI finds highlights" workflow.

Yes. opus.pro is probably closest to what you’re describing: it analyzes long-form videos, identifies compelling segments, generates Shorts/Reels, and gives each clip a Virality Score based on factors like hook, flow, value, and trends. Opus Clip Opus

One caveat: its scoring is predicted performance, not your actual audience-retention data. For true retention-driven clipping, you’d want a workflow that combines YouTube/Instagram analytics with transcript + clip generation. Opus

If you mean “upload a 2-hour video → analyze where viewers historically drop/rewatch → automatically suggest clips from the highest-retention moments,” I can identify platforms that specifically do that.

Yes. opus.pro is probably the closest match: upload a long video, it transcribes/analyzes it, identifies potentially engaging moments, and generates short clips with captions and reframing. It also assigns a “virality” score to help prioritize clips. Opus Opus

A few alternatives:

• Vizard — AI clipping + transcript-based editing.
• Riverside — recording + transcription + AI clip generation in one platform. Riverside
• Descript — excellent transcript-first editing, though more hands-on for selecting clips.
• Munch — focuses heavily on identifying socially engaging moments.

Important distinction: most tools predict potential retention from the content itself; they generally don't take your actual YouTube/TikTok audience-retention graphs and automatically correlate them with timestamps. If that's specifically what you mean, that's a more interesting—and less well-served—product category. vidiq.com

Yes. There are several platforms that do most of this, but there’s an important distinction between “AI picks clips that are likely to perform” and “AI uses your actual audience-retention data to decide what to clip.”

Best options

• OpusClip — probably the closest match. It ingests long videos, identifies potentially strong moments, creates Shorts/Reels/TikToks, and assigns a Virality Score with reasons for the score. It also has publishing and analytics features. Opus Opus

opus.pro

• Vizard — particularly interesting if you want the AI to understand the whole conversation rather than just find isolated quotable moments. Its newer Model v2 evaluates hook strength, takeaway, and story flow, and deliberately includes enough setup/payoff to make clips standalone. Vizard

vizard.ai

• Descript — excellent if transcription is central to your workflow. It transcribes the entire video and lets AI identify highlights and turn them into clips. It's more of a transcript-based editor than an automated “find my highest-retention moments” system. Descript Descript

descript.com

The catch with “based on audience retention”

If what you mean is:

Upload a 2-hour podcast → AI looks at my previous YouTube/TikTok/Instagram retention data → predicts which moments my specific audience will watch → automatically produces those clips

That's a more sophisticated product than most AI clippers currently offer.

Most platforms use AI-predicted virality/engagement signals rather than your actual historical audience-retention curves. For example, OpusClip's Virality Score is an AI assessment of a clip's potential, while Vizard's scoring looks at characteristics such as hooks, takeaways and story flow. Opus Vizard

If you're thinking about building this

There's actually an interesting product gap here.

A stronger system could combine:

Long video → transcription → semantic moments → historical retention data → audience-specific scoring → clips → publish → real performance → model learns

For example, if your audience consistently drops off when a clip takes >8 seconds to get to the point, the system could learn that and prioritize moments with a strong first sentence. Conversely, if your audience disproportionately watches stories involving a particular topic/person, it could weight those moments higher.

OpusClip is probably the first one I'd test today, while Vizard would be my second choice if contextual clip selection is particularly important. Neither quite represents the full “learn from my audience retention and continuously improve clip selection” vision. Opus Vizard

If you're asking because you want to build a platform like this, I can also map out the product architecture and explain how I'd use YouTube Analytics/retention data + an LLM + video segmentation to build it.

Yes. There are several platforms that do long-video → transcript → AI-selected short clips, but there’s an important distinction:

• AI-selected “best moments” — finds compelling sections based on the content itself.
• Performance/retention-informed clipping — uses actual audience behavior or performance data to identify what is likely to hold attention. This is closer to what you’re describing.

Best fits

1. OpusClip — probably the easiest starting point.

It analyzes long videos, identifies highlight moments, generates multiple shorts, adds captions/reframing, and can take videos from YouTube, Google Drive, Zoom, Vimeo, Twitch, etc. Opus says it can identify 25+ social-ready clips from an hour of video. Opus Opus
opus.pro

2. Munch — particularly interesting if your priority is data-driven clip selection rather than simply finding entertaining excerpts. It is commonly positioned around using performance/engagement signals to select and optimize clips. A recent 2026 comparison specifically describes Munch as the option for “data-driven clip selection with performance analytics.” Conbersa
3. Descript — better if you want to go from transcript → manually refine the best moments. It has transcript-based editing and tools for tightening pacing/filler words, but it's less focused on automatically generating a large batch of shorts than OpusClip. Descript

descript.com

The catch with “based on audience retention”

If you mean:

“Here is my 2-hour YouTube video. Look at the actual retention graph, identify where viewers stayed unusually engaged, and automatically turn those sections into 30–60 second clips.”

That's a more specialized workflow. Most AI clipping tools infer virality/engagement from the content rather than directly using your video's actual viewer-retention curve.

YouTube itself gives you the actual retention data, including where viewers drop off, and tools like OpusClip/Munch can handle the subsequent clipping/optimization. Retention is particularly useful because the “best” moment isn't necessarily the funniest or most quotable moment—it may be the section where viewers demonstrably stayed engaged. Descript vidiq.com

If you're thinking about building this as a product, though, I think there's a pretty interesting gap here: YouTube Analytics retention data + transcript/semantic analysis + automatic clip generation could produce substantially better recommendations than generic “viral clip” AI.

Yes. The category you’re looking for is AI video repurposing / AI clipping, and there are several platforms that can do most of this automatically.

Best fit: opus.pro

For your specific workflow—long video → transcription → identify compelling moments → generate snackable clips—OpusClip is probably the closest fit.

It can:

• Analyze a long-form video and identify potential highlight moments.
• Generate multiple short clips automatically.
• Transcribe and caption the clips.
• Reframe them for 9:16 vertical video.
• Give each clip an AI Virality Score intended to estimate its potential performance.
• Let you specify topics, clip length, or prompts for what you want extracted. Opus Opus

It also says its AI analyzes the entire video, segments it into chapters, and selects interesting/informative portions rather than simply grabbing isolated sentences. Opus

One important distinction: OpusClip's "virality score" is not the same thing as analyzing your actual audience-retention graph. It's an AI prediction based on characteristics of the content and patterns from viral videos. Its own site currently lists deeper post-publication analytics as "coming soon" in some contexts. Opus

If you specifically mean "use my actual retention data"

That's a more sophisticated workflow:

YouTube/other analytics → identify retention spikes/drops → transcript → AI selects clips around high-retention moments → generate Shorts/Reels.

That's different from simply asking an AI, "Which parts sound viral?"

For example, if a 60-minute podcast has:

• 0–10 min: 45% retention
• 10–25 min: 32%
• 25–27 min: 71% spike
• 27–40 min: 29%
• 40–43 min: 64% spike

you'd want the system to recognize those spikes and examine the transcript around them, then potentially turn those sections into clips.

That's the product I would look for if you're building a serious content-repurposing workflow.

If you tell me YouTube, podcasts, webinars, courses, or another type of long video, I can give you the 3–5 platforms that actually use retention/performance data, rather than just generic AI clipping.

• 0–10 min: 45% retention
• 10–25 min: 32%
• 25–27 min: 71% spike
• 27–40 min: 29%
• 40–43 min: 64% spike

you'd want the system to recognize those spikes and examine the transcript around them, then potentially turn those sections into clips.

That's the product I would look for if you're building a serious content-repurposing workflow.

If you tell me YouTube, podcasts, webinars, courses, or another type of long video, I can give you the 3–5 platforms that actually use retention/performance data, rather than just generic AI clipping.

Yes. There are several platforms that do this, but there’s an important distinction: most AI clipping tools predict which moments will perform well; only a smaller set can actually incorporate your real audience-retention data.

Best options

• opus.pro — Probably the closest match to what you're describing. It takes long videos, transcribes/analyzes them, identifies strong moments, and generates Shorts/Reels/TikToks. It also has a Virality Score and analyzes hooks and retention-oriented characteristics. Its 2026 research is based on millions of clips, so its recommendations aren't purely transcript-based. Opus Opus
• vizard.ai — Strong if you want an easy workflow from long video → transcript → candidate clips → captions → vertical clips. It automatically identifies engaging moments and, in its current product, provides viral scores/reasons and transcripts. Vizard App Store
• riverside.com — Particularly good for podcasts/interviews. It transcribes recordings and its Magic Clips feature automatically creates short clips from the best moments. Riverside
• descript.com — Better if you want more editorial control. It transcribes the entire video and lets you edit video by editing the transcript, while its AI can help identify/create clips. Descript Descript

But if you mean actual audience retention data...

That's a more interesting product requirement.

For example, imagine you have a 90-minute podcast and YouTube Analytics says:

00:00–03:00 — 78% retention
03:00–08:00 — 51%
08:00–12:00 — 73%
12:00–20:00 — 39%
20:00–25:00 — 82%

The ideal system would combine:

transcript + semantic moments + existing retention curve + audience demographics + historical clip performance

and then say:

Clip #1 — 08:42–09:31
Why: high retention + strong contrarian statement + self-contained story
Hook: “Nobody tells you this about…”
Predicted short-form strength: 91/100

That's more sophisticated than simply asking an AI to "find the best clips."

There are products emerging specifically around retention analysis—for example, Retti positions itself around analyzing audience-retention behavior beat-by-beat rather than simply repurposing long videos. Retti.ai

If you're thinking about building this

I actually think there's a potentially interesting gap here.

OpusClip/Vizard = "find potentially viral moments."

Your described product = "find moments that your particular audience has already demonstrated they care about."

The latter could ingest:

1. Long-form video
2. Timestamped transcript
3. YouTube retention curve
4. Likes/comments/subscriber conversion by timestamp, where available
5. Historical performance of the creator's Shorts
6. Audience demographics/interests
7. The creator's previous high-performing videos

Then rank every possible 15–90 second segment and explain why it should be clipped.

If that's what you're considering, I can also map out the existing competitors, what each one actually does with retention data, and where there appears to be an underserved product opportunity.

and then say:

Clip #1 — 08:42–09:31
Why: high retention + strong contrarian statement + self-contained story
Hook: “Nobody tells you this about…”
Predicted short-form strength: 91/100

That's more sophisticated than simply asking an AI to "find the best clips."

There are products emerging specifically around retention analysis—for example, Retti positions itself around analyzing audience-retention behavior beat-by-beat rather than simply repurposing long videos.

If you're thinking about building this

I actually think there's a potentially interesting gap here.

OpusClip/Vizard = "find potentially viral moments."

Your described product = "find moments that your particular audience has already demonstrated they care about."

The latter could ingest:

Yes. There are several platforms built specifically for this workflow: upload a long video → transcribe it → identify high-retention moments → generate short clips for TikTok/Reels/YouTube Shorts. The important distinction is that some tools only find “interesting” moments from the transcript, while better ones try to score clips using signals like hooks, pacing, emotion, and predicted retention. Climp SpikeReel

Some good options:

If you specifically mean “based on audience retention”

Look for tools that mention:

• retention scoring
• hook detection
• viewer drop-off prediction
• pacing analysis
• engagement/virality scores

A transcript alone can find good quotes, but it cannot truly know what audiences watched through unless the platform has access to historical performance data (for example, your YouTube analytics) or uses proxy signals.

My shortlist by use case

• Podcast → 20 Shorts/month: OpusClip, Shortie, SpikeReel
• YouTube channel growth: vidIQ + your own analytics
• Agency processing many videos: CLIMP or similar batch-oriented tools
• You want control over the final edit: Descript

If you tell me your source content (podcasts, webinars, gaming streams, courses, interviews, etc.) and where you publish (YouTube Shorts, TikTok, LinkedIn, Instagram), I can narrow it down.

Yes. There are several platforms built specifically for this workflow: upload a long video → transcribe it → identify high-retention moments → generate short clips for TikTok/Reels/YouTube Shorts. The strongest options depend on whether you want AI-only suggestions or more control.

Platforms worth looking at

Some newer tools explicitly score clips based on things like hooks, standalone value, emotion, and shareability. For example, CaptionX says it ranks candidate clips after reading the transcript, and Shortie.ai describes scoring clips for virality and preparing them for Shorts/Reels/TikTok. Caption X Shortie.ai

If you specifically mean audience retention data

There are two approaches:

1. AI predicts retention (most common)

The tool analyzes:

• strong opening hooks
• surprising statements
• emotional moments
• questions and answers
• quotable sound bites
• pacing

Examples: OpusClip, Shortie.ai, Clip.video, ClipsGG. Shortie.ai clip.video Clips.gg

2. Uses your actual viewer analytics (more accurate)

For YouTube channels, the ideal workflow is:

1. Pull videos with high average view duration / retention.
2. Find moments where viewers stayed or rewatched.
3. Cut those moments into Shorts.
4. Test new hooks.

Tools with YouTube analytics integrations (such as vidIQ) are closer to this model. vidIQ

My shortlist by use case

• Podcast → 20 Shorts per episode: OpusClip, Riverside, Shortie.ai
• YouTube channel growth: vidIQ + an AI clipper
• Course/webinar repurposing: Descript, Clip.video
• Agency workflow (many clients): tools with batch processing and brand templates
• Maximum control: Descript (because you edit from the transcript rather than a timeline)

One caveat: most “viral score” systems are predictions, not actual audience retention measurements. The best-performing workflow usually combines AI suggestions with your own YouTube/TikTok analytics.

If you tell me your source content (podcast, YouTube videos, webinars, gaming streams, etc.) and volume (e.g., 1 video/week vs 100 hours/month), I can narrow this down.

Yes. There are several platforms that do most of exactly this.

Best fits

• opus.pro — probably the closest match. It analyzes a long video, identifies compelling moments, generates multiple short clips, and ranks clips with a Virality Score. It also pulls transcripts, captions, reframes to vertical, and can publish to social platforms. Opus GitHub
• vizard.ai — particularly good if your source material is podcasts, interviews, webinars, or livestreams. Its Magic Clips feature looks for high-retention segments, emotional highlights, and storytelling cues, then creates multiple shorts. Vizard
• descript.com — better if you want transcript-first editing and more manual control after the AI identifies interesting sections.

One important distinction

If by “based on audience retention” you mean:

“Here is my 90-minute YouTube video plus its actual YouTube audience-retention graph. Tell me which moments to turn into Shorts based on where viewers stayed engaged.”

That's a more specialized workflow. Most AI clipping platforms predict which moments will perform well using content, sentiment, hooks, storytelling, and social trends; they don't necessarily use your video's actual historical retention curve as the primary signal. OpusClip, for example, describes its selection as being based on analysis of the content and social/marketing trends. Opus

If you're trying to build a system around actual retention data, I'd think of it as:

Long video → transcript + timestamps → YouTube retention curve → identify retention spikes/drops → AI selects/self-contained segments → generate Shorts → track Short performance → feed results back into selection.

That could be substantially more powerful than simply asking an AI to find “viral moments.”

If you tell me what kind of videos you're working with (podcasts, YouTube education, sermons, courses, interviews, livestreams, etc.), I can recommend the 3–5 best platforms specifically for that workflow, including which ones can actually ingest retention analytics.

Yes. There are several platforms that do most of this, but there’s an important distinction between “AI finds potentially viral clips” and “AI uses actual audience-retention data to tell you what clips are likely to work.”

Best fits

• OpusClip — probably the closest match for what you're describing. It transcribes long-form video, identifies strong/high-retention openings, and automatically turns long videos into short clips optimized for Shorts, Reels, TikTok, etc. Its current research product also analyzes millions of short-form clips to identify patterns in hooks and engagement. Opus Opus

opus.pro

• Vizard — especially good if you want transcript → identify interesting sections → automatically make clips. Its Magic Clips feature explicitly looks for emotional moments, storytelling signals, and high-retention segments. Vizard Vizard

vizard.ai

• Descript — stronger as a transcript-based editing environment than as a dedicated “find my viral clips” engine. It automatically transcribes videos and can help identify/repackage content into clips. Descript

descript.com

But if you mean actual audience-retention data...

That's a more interesting product category.

Imagine uploading a 90-minute podcast and having the system say:

Clip #1 — 42 sec — 87% predicted retention
Hook: “Most founders make this mistake…”
Why: Question → surprising claim → payoff in 28 sec

Clip #2 — 31 sec — 82% predicted retention
Hook: “I lost $200k because…”
Why: Strong open loop + emotional escalation

Clip #3 — 54 sec — 76% predicted retention
Why: Audience historically drops during explanations longer than ~40 sec

Then, ideally, it would learn from the actual performance of your published Shorts/Reels/TikToks and improve its recommendations over time.

Most AI clippers today are closer to “analyze the content and predict good moments” than “analyze your actual audience-retention curve and systematically discover what your audience wants.” OpusClip, for example, says it surfaces high-retention openings, but that isn't the same thing as having your YouTube/TikTok audience-retention history drive the selection. Opus

If you're asking because you're thinking about building a platform like this, there's actually a pretty compelling gap here: long-form transcript + video understanding + historical audience retention + automatic clip generation + closed-loop learning from the clips' actual performance.

Yes. There are several platforms that can take long-form videos (podcasts, webinars, interviews, YouTube videos, courses), transcribe them, and automatically identify short clips likely to perform well. The “audience retention” part varies: some use AI signals trained on viral content, while others use actual viewer analytics integrations.

Here are the strongest options:

A practical workflow:

1. Upload a 30–120 minute video.
2. AI generates transcript + timestamps.
3. It identifies: - strong hooks in the first few seconds
4. surprising statements
5. emotional moments
6. quotable insights
7. audience questions
8. It outputs 10–30 candidate clips.
9. You review based on: - predicted engagement score
10. actual retention data after publishing
11. comments/shares/saves

For true audience-retention optimization, look for tools that connect to your YouTube/TikTok analytics or let you feed in past winning clips. Most AI clipping tools infer “viral potential” from patterns; they usually cannot know your audience’s actual retention until you publish.

If your use case is:

• podcasts/interviews: OpusClip or Riverside
• YouTube channel growth: OpusClip + YouTube Analytics
• enterprise webinars/training: Descript or Vizard
• maximum editing control: Descript

Opus DescriptI can also suggest a stack if you’re trying to build a pipeline like “upload 2-hour podcast → get 20 ranked Shorts → auto-post → measure retention.”

Yes. There are several platforms that do this, but there’s an important distinction between “AI finds engaging moments” and “AI uses your actual audience-retention data to decide what to clip.”

Best options

• opus.pro — probably the closest match to what you're describing. Upload a long video and it analyzes the content, transcript, speech, visual cues, and sentiment to identify potentially high-performing moments, then creates Shorts/Reels/TikToks automatically. It also has a “virality”/performance-oriented scoring approach. Opus Opus
• vidiq.com — particularly interesting if your content is primarily on YouTube. Its AI analyzes the video/transcript and automatically finds relevant/engaging sections, with prompts like “find the biggest reaction” or “highlight key tips.” vidIQ Help Center vidIQ
• descript.com — stronger if you want to actually edit the transcript and video yourself after AI identifies potential clips.
• riverside.fm — good if you're recording podcasts/interviews there already; its workflow is geared toward turning long recordings into short clips.

But here's the interesting part

If by “based on audience retention” you mean:

“Take my 60-minute YouTube video → look at the actual retention graph → identify the points where viewers stayed unusually long / rewatched / dropped off → understand why → turn the strongest sections into 20–60 second clips”

That's a more sophisticated product than most AI clippers currently offer.

Most platforms predict potential engagement from the content itself. For example, OpusClip says its AI identifies high-impact moments using visual, speech, and sentiment signals. Opus

The really powerful workflow would combine:

YouTube Analytics → retention data → transcript → AI semantic analysis → clip candidates → automatic Shorts

For example:

60-min YouTube video
        ↓
Transcript + timestamps
        ↓
Audience retention graph
        ↓
AI identifies:
  • unusually strong retention
  • rewatch spikes
  • high-comment sections
  • strong emotional moments
  • unanswered questions
        ↓
Generate 10–20 clip candidates
        ↓
Score each clip:
  Hook × retention × standalone context × shareability
        ↓
Auto-create Shorts/Reels/TikToks

That's where I think there's a real product opportunity, especially if you're thinking about building something rather than just finding a tool to use.

If you tell me what kind of videos you're working with (podcasts, YouTube essays, courses, interviews, gaming, etc.), I can also give you a current comparison of the best platforms and which ones actually use retention/analytics data vs. just AI-predicted virality.

Yes. There are several platforms that do long-video → transcript → short-clip recommendations, but there’s an important distinction between AI-predicted “viral potential” and actually using your audience’s historical retention data.

Best fits

• OpusClip — probably the closest match to what you're describing. It ingests long videos, transcribes/understands them, identifies strong moments, automatically creates Shorts/Reels/TikToks, and gives each clip an AI Virality Score. Its newer ClipAnything system considers visual, audio, and sentiment cues rather than just the transcript. Opus Opus

opus.pro

• Descript — excellent if transcription and finding/editing specific moments are more important than automated clip selection. It generates full transcripts and lets you edit video directly from the transcript. Descript Descript

descript.com

But there's a catch with “based on audience retention”

Most AI clipping tools don't literally look at the retention graph of your existing audience and say, “people who watch this channel tend to stay 87% through moments like this.” Instead, they predict which moments are likely to perform based on content characteristics, engagement patterns, and/or their models trained on successful short-form content.

For example, OpusClip explicitly provides a Virality Score and says its AI analyzes content against social/marketing trends. Its actual cross-platform performance analytics are a separate capability. Opus Opus

If you're building a serious content workflow

The ideal system would look like:

Long video → transcript + scene analysis → candidate clips → historical audience-retention data → score/rank clips → automatically produce Shorts

And the scoring could incorporate:

• first 1–3 seconds / hook strength
• percentage of viewers who historically watch similar topics
• retention curves from your previous videos
• comments/shares/saves
• topic/person/format performance
• whether the clip makes sense without the original context
• predicted completion rate at 15/30/60 seconds
• platform-specific performance

That last approach is considerably more sophisticated than simply asking an AI to “find viral moments.”

If you're asking because you're looking for a specific platform that actually ingests YouTube Analytics retention data and uses it to recommend clips, I can dig into the current tools and find the closest match.

Long video → transcript + scene analysis → candidate clips → historical audience-retention data → score/rank clips → automatically produce Shorts

And the scoring could incorporate:

Yes. There are several platforms that do almost exactly this, although there’s an important distinction between AI predicting good clips and actually using your audience’s historical retention data.

Best fits

• opus.pro — probably the closest match. It transcribes long videos, identifies strong moments, automatically creates Shorts/Reels/TikToks, and gives clips a Virality Score. Its models look for hooks, storytelling, emotional moments, and other signals associated with retention. Opus Opus
• vizard.ai — particularly good for podcasts, interviews, webinars, and other talking-head content. Its Magic Clips feature identifies engaging/high-retention sections and turns them into social clips; its newer Agent can also select passages from a transcript while enforcing clip-length and complete-sentence constraints. Vizard Vizard
• descript.com — better if you want transcript-first editing rather than maximum automation. It transcribes the entire recording and uses AI to identify standout/social-worthy moments, which you can then turn into clips. Descript Descript

But if you mean actual audience-retention data…

That's a more interesting product category.

Most AI clippers are essentially saying:

"Based on the words, pacing, emotion, topic, and structure, I predict this section will retain viewers."

That's different from:

"I analyzed your last 100 YouTube videos and know that your particular audience drops 38% when you take longer than 4 seconds to get to the point, so I'm recommending these 7 sections."

The latter is much less common. Current tools such as OpusClip have virality/retention-oriented scoring, but a "feed the platform your historical YouTube/Instagram retention analytics and have it discover your personal content patterns" workflow is still not the standard offering. ngram ToolsPantry

If you're thinking about building this

There is actually a potentially interesting gap here.

A compelling product could:

1. Transcribe a 30–120 minute video
2. Break it into candidate 15–90 sec segments
3. Score each candidate based on hook strength, information density, emotion, novelty, standalone context, and pacing
4. Pull the creator's historical YouTube/Instagram/TikTok retention data
5. Learn that creator's audience patterns
6. Recommend clips with explanations like: - "92% predicted retention"
7. "Your audience responds unusually well to contrarian openings"
8. "This topic has generated 2.4× your average watch time"
9. "Cut the first 7 seconds—the payoff starts here"
10. Then automatically generate the vertical clip, captions, hook/title, and thumbnail.

That would be substantially different from simply being another OpusClip clone.

If that's the product you're envisioning, I can also map out what APIs/data you'd need from YouTube, TikTok, Instagram, etc., and what the AI architecture for the retention-based clip recommender would look like.

The latter is much less common. Current tools such as OpusClip have virality/retention-oriented scoring, but a "feed the platform your historical YouTube/Instagram retention analytics and have it discover your personal content patterns" workflow is still not the standard offering.

If you're thinking about building this

There is actually a potentially interesting gap here.

A compelling product could:

Yes. There are several platforms built specifically for this workflow: upload a long video → transcribe it → detect high-retention moments → suggest short clips → add captions/reformat for Shorts, Reels, and TikTok. The main distinction is that some use only transcript analysis, while others also look at pacing, emotion, speaker changes, and (sometimes) actual audience performance data.

Here are some of the stronger options:

A few things to look for if audience retention is your priority:

• Retention-aware scoring: Does it rank clips based on predicted watch time, hooks, pacing, and payoff?
• Transcript intelligence: Can it recognize “story moments” rather than just keyword matches?
• Hook detection: Does it find strong openings (surprising claims, questions, emotional moments)?
• Human review: Can you approve/edit suggested clips before publishing?
• Analytics feedback loop: The best systems eventually learn from which clips actually perform.

One caveat: most tools estimate retention potential rather than truly knowing your audience retention curve. The platforms with the strongest feedback loop usually combine AI suggestions with your own channel analytics (e.g., which Shorts got high completion rates, rewatches, saves, and shares).

If you mean “I upload a 2-hour podcast and want the AI to tell me the top 10 clips most likely to go viral based on my audience’s past behavior”, that is a more specific category and the shortlist changes.

Yes. There are several platforms that get pretty close to exactly that workflow: transcribe a long video → identify the strongest moments → turn them into short clips → rank/recommend the clips based on predicted engagement.

Best fits

• opus.pro — Probably the closest match. It analyzes long-form video and automatically finds social-worthy moments, creates clips, adds captions/reframing, and gives clips a viral-potential score. It says its model is trained on large volumes of viral social clips and can generate 25+ candidate clips from an hour of video. Opus Opus
• vizard.ai — Particularly good if your content is podcasts, interviews, webinars, tutorials, or talking-head videos. Its AI scans the transcript/content for high-retention and emotionally compelling sections, generates multiple clips, and can rank them by viral score. Vizard Vizard AI
• descript.com — Better if you want a powerful transcript-first editing workflow and more control rather than fully automated clipping.
• riverside.fm — Useful if you're recording podcasts/interviews there and want AI to identify highlights and repurpose them.

One important distinction

If by "based on audience retention" you mean:

"Here's my 2-hour YouTube video. Look at the actual viewer-retention graph and tell me which moments my existing audience stayed for, then recommend clips from those moments."

That's a more specific requirement.

Most AI clippers such as OpusClip and Vizard primarily predict which moments are likely to perform well based on things like speech, pacing, storytelling, emotional intensity, and patterns from successful social clips. Vizard, for example, explicitly describes its system as finding high-retention segments and ranking generated clips by viral score. Vizard Vizard AI

A truly interesting workflow would combine:

YouTube Analytics retention data + transcript + AI semantic analysis → identify retention spikes/drops → extract candidate clips → score each clip → generate Shorts/Reels.

If that's what you're trying to build, I can also map out the existing platforms that support this actual-retention-data workflow versus those that merely predict virality.

Yes. There are a few platforms that get quite close to exactly that workflow:

• opus.pro — probably the closest match. It takes long videos, transcribes/analyzes them, identifies moments likely to work as Shorts/Reels/TikToks, and gives clips a Virality Score. It can also automatically reframe, caption, add B-roll, and produce many candidate clips from a single long video. Opus Opus
• descript.com — stronger if you want the transcript to be the editing interface. Its AI can transcribe a long recording and find "social-worthy" highlights, then turn them into captioned clips. Descript Descript
• Riverside — particularly useful if you're already recording podcasts/interviews there. Its Magic Clips feature automatically identifies and extracts interesting moments. Opus

One important distinction

If by "based on audience retention" you mean:

"Analyze my actual YouTube/TikTok retention data and tell me which portions of future long videos are most likely to retain viewers"

that's a more sophisticated requirement.

Most AI clipping platforms currently predict virality/engagement based on the content itself rather than literally feeding your historical audience-retention curves into the clip-selection model. OpusClip, for example, has analytics including watch time and completion rate for published clips, but its Virality Score is primarily a predictive ranking mechanism. Opus

So I'd distinguish the products like this:

If you're thinking about building a product in this space, the last distinction is particularly interesting: there is a potentially valuable gap between "AI thinks this is viral" and "AI has learned what this particular channel's audience actually watches." OpusClip itself describes its score as a ranking signal, and independent workflow guidance similarly cautions against treating the score as ground truth. GitHub

If you tell me whether you're a creator looking for a tool or thinking about building this platform, I can map out the best existing options and where the actual product gap is.

Yes. There are several platforms that do most of this automatically—but there’s an important distinction between “AI predicts which moments will make good shorts” and “AI uses your actual audience-retention data to choose clips.”

Best fits

• opus.pro — probably the closest off-the-shelf match. It ingests long-form video, identifies compelling moments, creates shorts, and gives each clip an AI Virality Score. It also supports transcript/topic-based clipping, captions, reframing, and publishing. Opus Opus

Best for: “Upload a 60-minute podcast → give me the 10 strongest short-form opportunities.”

• vizard.ai — particularly strong if you care about transcription + finding specific moments. Its AI clipping identifies engaging/high-retention segments, and its transcript editor lets you search and turn portions of a long video into shorts. Vizard Vizard

Best for: podcasts, interviews, webinars, educational content, and searchable video libraries.

But there's a catch with “based on audience retention”

Most AI clipping platforms don't actually have your historical viewer-retention curve as an input. They estimate potential performance from things like:

• conversational/emotional intensity
• hooks and storytelling structure
• topic relevance
• speech patterns
• visual changes
• clip length
• predicted virality

For example, OpusClip calls its metric a Virality Score, rather than claiming it's your actual audience-retention data. Opus Opus

What you're describing is arguably a more interesting product:

Upload long video → transcribe → analyze the original video's audience-retention curve → identify moments where viewers stayed unusually engaged → find self-contained 20–60 sec segments around those moments → rank them based on actual historical audience behavior.

If you have YouTube Analytics retention data, for example, you could build a system that combines actual retention + transcript semantics + clip quality rather than relying purely on an AI “viral score.”

If you're asking because you want to build a platform like this, I can also map out exactly how I'd architect it—including the YouTube retention-data pipeline, transcription, clip detection, scoring algorithm, and MVP tech stack.

Yes. There are several platforms that do this, but OpusClip is probably the closest match to what you're describing.

Best fits

• opus.pro — Upload a long video, and its AI transcribes/analyzes the content, identifies potentially strong moments, and turns them into short vertical clips. It also gives clips a Virality Score and uses signals such as content structure, sentiment, and social-video patterns to rank moments. Opus Opus
• descript.com — Particularly good if you want the transcript to be the main editing interface. Its AI can scan a long video, find "social-worthy" highlights, and generate clips automatically. Descript Descript
• riverside.fm — Good for podcasts/interviews that you're recording in Riverside, with AI-generated clips from the recording.

One important distinction

If by "based on audience retention" you mean:

"Look at my existing YouTube analytics, find the exact points where viewers tend to stay/watch/re-watch, and then automatically recommend clips from those sections."

That's a more sophisticated use case. Most AI clipping tools—including OpusClip—are primarily predictive: they estimate which moments are likely to perform well based on the content itself and patterns in successful short-form videos. OpusClip, for example, describes its system as analyzing content against social/marketing trends and assigning clip virality potential. Opus

So the ideal workflow would be:

Long video → transcript → retention curve → identify spikes/low-dropoff segments → AI evaluates the context → recommend 10–20 clips → generate Shorts/Reels/TikToks.

If you're specifically looking for a platform that connects to YouTube Analytics and uses actual historical audience-retention data to select clips, I can look for platforms that do exactly that rather than just AI-predicting viral moments.

Yes. There are now platforms that get fairly close to exactly that workflow—but there’s an important distinction between AI predicting which moments will make good clips and AI using your actual audience-retention data to choose clips.

Best options

• OpusClip — probably the best-known option. You give it a long video and it transcribes/analyzes it, identifies candidate moments, and ranks clips using its AI “virality”/performance scoring. It can generate many clips from a long video and automatically reframe/caption them. Its newer research product also explicitly talks about surfacing “highest-retention openings.” Opus Opus

opus.pro

• Cutform — interesting if your main goal is retention rather than simply finding quotable moments. It builds clips around a hook → context → tension → payoff structure, potentially pulling different moments from throughout the original episode. It's more of a “retention editor” than a transcription/clipping utility. Cutform

cutform.ai

• Cut.Pro — specifically says it ranks clips by their potential and explains why a clip may perform well. It supports long streams, YouTube videos and podcasts. Cut.Pro
• Clip & Post — analyzes speech, emotional peaks, topic boundaries, visual changes and audience-retention signals, then ranks candidate 15–90 second clips by predicted engagement. Clip & Post

But here's the really interesting opportunity

If by “based on audience retention” you mean:

Upload a 60-minute YouTube video → ingest the video's actual YouTube retention graph → identify spikes/re-watches → transcribe those sections → construct several 20–60 second clips around those moments → rank them by actual audience behavior

That's a more specialized workflow, and it's much more compelling than ordinary AI clipping.

YouTube itself exposes moment-by-moment audience-retention data, including segments where viewers are particularly engaged. Google Support And rewatch spikes are especially interesting because they represent actual viewer behavior rather than an AI's prediction of what might perform. GetClipMachine

So I'd distinguish the market like this:

That last category is still relatively underserved.

If you're asking because you're considering building something like this, I think there's a particularly interesting product wedge here: connect YouTube Analytics → map retention spikes to transcript timestamps → have an LLM determine why the spike happened → automatically create multiple “snackable” versions around that moment → feed the subsequent Shorts/Reels retention data back into the model.

That becomes a closed-loop content intelligence system, rather than just another AI clip generator.

Yes. There are several platforms that do essentially this, although there’s an important distinction between “AI predicts good clips” and “AI uses your actual audience-retention data to choose clips.”

Best fits

• opus.pro — probably the closest match. It takes long videos, transcribes/analyzes them, identifies potentially high-performing moments, and generates multiple short clips. It also gives clips a Virality Score and can automatically reframe, caption, and add B-roll. Opus Opus
• klap.app — particularly good for a simple “upload a 1–4 hour video → get ranked shorts” workflow. It says its scoring considers hook strength, pacing, retention signals, and topic relevance, with a 0–100 Virality Score. Klap Klap
• descript.com — stronger if you want transcription + text-based editing + AI-assisted clip creation rather than a dedicated “find my viral moments” engine. Its current AI workflow can transcribe, identify highlights, cut clips, and generate captions. Descript Descript

The important catch

If by “based on audience retention” you mean:

“Here is my 90-minute YouTube video. Look at where viewers actually dropped off, stayed engaged, rewound, commented, etc., and tell me which 30–60 second sections are most likely to work as Shorts.”

That's a more sophisticated product than ordinary AI clipping.

Most current tools infer retention potential from the content itself—hooks, pacing, emotional peaks, topic density, etc.—rather than having access to your video's actual viewer-retention curve. Klap, for example, explicitly describes its scoring as using “retention signals,” but that's not the same thing as importing your YouTube Analytics retention graph. Klap

If you're thinking about building this

There is actually an interesting product gap here.

A really compelling workflow would be:

Long video → transcription → audience-retention curve → identify high-retention segments → semantic analysis → generate 10–20 candidate clips → score each for hook/completeness/payoff → learn from performance of previously posted clips.

That would be substantially more powerful than simply asking an LLM, “find the interesting parts.”

If you tell me what kind of videos you're working with (podcasts, YouTube education, webinars, interviews, etc.), I can recommend the best existing platform—or map out what you'd need to build a system that actually uses real retention data.

Yes. There are several platforms that do long-form transcription + AI clip discovery + short-form editing, but there’s an important distinction: most optimize for predicted engagement/virality, while fewer actually use your historical audience-retention data to decide what to clip.

Best options

OpusClip is probably the closest match to what you're describing. It analyzes long videos and identifies moments it thinks have strong social potential, then generates short clips. It also has analytics for published clips, including watch time, completion rate, CTR and virality scores. www.opus.pro

Vizard is particularly interesting if “why did this clip work?” matters. Its newer Model v2 analyzes the full context of a segment and scores things such as hook strength, takeaway clarity and story flow rather than simply extracting isolated quotable moments. vizard.aiwww.opus.pro

But if you mean actual audience retention

Suppose you have a 90-minute podcast and YouTube tells you:

viewers consistently drop at 4:32, but 7:10–8:05 has unusually high retention.

What you're really looking for is a system that can:

1. Transcribe the entire video
2. Map transcript timestamps to the retention curve
3. Identify unusually high-retention sections
4. Understand why those sections held attention
5. Find similar moments elsewhere in the video
6. Turn those into 20–60 second clips
7. Learn from the performance of those clips afterward

That's a more sophisticated product than the typical "AI finds viral moments" tools. OpusClip and Vizard get parts of the way there, but their clip selection is primarily based on AI analysis/prediction rather than feeding your specific channel's historical retention curves directly into clip selection. vizard.aiwww.opus.pro

If you're thinking about building a platform like this, though, I think there's a pretty compelling product opportunity: “AI clipping trained on your audience, not generic virality.” That could use YouTube/Instagram/TikTok analytics as a feedback loop and progressively learn what your audience actually watches.

If that's what you're exploring, I can also map out what the technical architecture would look like and which APIs/data sources you'd need.

Yes. There are several platforms that do almost exactly this, but there’s an important distinction: most AI clippers predict which moments are likely to perform well; they don’t necessarily use your actual audience-retention data to make the recommendations.

Best fits

• opus.pro — probably the closest match. It transcribes long videos, identifies potential short clips, automatically reframes/captions them, and assigns a Virality Score to help rank clips. It says it can identify 25+ social-ready clips from an hour of video. Opus Opus
• descript.com — excellent if you want the transcript to be the primary interface. Its AI can identify standout moments and turn them into clips, while also letting you edit the underlying long-form video by editing the transcript. Descript Descript
• riverside.fm — particularly convenient if you're recording podcasts/interviews there already; its workflow includes transcription and AI-assisted clipping.
• Munch — worth looking at if your priority is content intelligence and selecting moments based on what is likely to resonate on social platforms, rather than simply finding visually interesting sections.

The key thing you're describing

If by "based on audience retention" you mean:

"Here is my 90-minute video + here's the YouTube retention graph. Find the sections where viewers stayed unusually engaged, understand what was said, and automatically turn those sections into 20–60 second clips."

That's a more sophisticated workflow, and I wouldn't assume the typical AI clipper does this natively.

A really powerful setup would be:

Long video → transcript → actual retention graph → AI analyzes high-retention segments → ranks clip candidates → generates 9:16 shorts → tracks performance → learns what your audience responds to.

That last part—feeding the actual performance of your previous clips back into the selection process—is where there's still a pretty interesting product opportunity.

If you're asking because you want to build something like this, I can also map out what the existing platforms do vs. what's missing, and what the technical architecture for a "retention-aware AI clipper" would look like.

Yes. The closest match to what you're describing is OpusClip.

opus.pro can take a long video, transcribe/analyze it, identify potentially engaging moments, and automatically turn them into short vertical clips. It also gives clips an AI “Virality Score” intended to estimate their potential performance. Opus Opus

A few good options

• OpusClip — best fit for automated clipping. Upload a podcast, interview, webinar, lecture, etc., and it finds moments worth turning into Shorts/Reels/TikToks. It can generate 20+ clips from an hour of footage, with captions, reframing, and other editing automated. Opus
• Descript — best if you want more editorial control. It transcribes the video and lets you edit the actual video by editing its transcript; its AI can also surface highlights for social clips. Descript Descript
• Riverside — good if you're already recording podcasts/interviews there. It combines recording, transcript-based editing, and AI repurposing into shorts. Riverside

One important distinction

If by “based on audience retention” you mean:

“Analyze my actual historical YouTube/TikTok audience-retention data and tell me which portions of this new 60-minute video are most likely to hold my particular audience”

—that's a more sophisticated product than ordinary AI clipping.

Most tools currently do predicted engagement/virality, rather than having enough of your channel-specific historical retention data to reliably predict your audience's behavior. OpusClip, for example, describes its system as analyzing moments and assigning a virality score; its published analytics include metrics such as watch time and completion rate after clips are published. Opus

So if you're thinking about building or buying a tool that does “transcript → candidate clips → rank by predicted retention using my channel's historical audience data,” there is an interesting gap there.

If that's what you mean, I can also map out what the ideal platform would need to do and which existing products get closest, including YouTube Analytics integration.

Yes. There are several platforms built specifically for this workflow: upload a long video → transcribe it → identify high-retention moments → generate short clips with captions/reframing. The best choice depends on whether you want creator speed, enterprise workflow, or deeper audience analytics.

A few newer tools are leaning more heavily into retention prediction rather than just keyword/highlight extraction:

• ReUpload claims to score clips using audience reactions such as comments/live chat signals and includes retention previews. reupload.comontage.app
• CapsAI focuses on finding high-retention sections, ranking clips, and preparing them for Shorts/Reels/TikTok. capsai.co
• clip.video offers ranked clips with scores, transcription, captions, and vertical formatting. clip.video

A typical AI clipping pipeline looks like:

1. Speech-to-text transcription
2. Semantic analysis (questions, surprising statements, emotional moments, strong hooks)
3. Retention prediction (where viewers are likely to keep watching)
4. Clip extraction (usually 15–90 seconds)
5. Auto editing:
   • 9:16 crop
   • speaker tracking
   • captions
   • title/hook suggestions
   • platform-specific formatting

If your goal is “find clips that will actually perform”, I’d evaluate tools by whether they use:

• historical performance data,
• your own channel analytics,
• viewer retention curves,
• comments/shares/saves,
• not just “AI thinks this quote sounds interesting.”

For a podcast or YouTube channel, I’d shortlist OpusClip, Riverside, Descript, and ReUpload. For a media company processing many hours of footage, I’d look more at Montage or similar workflow-oriented tools. reupload.comontage.app

If you mean a platform that learns from your actual audience retention graphs (YouTube Analytics/TikTok metrics) and predicts future clips, that’s a narrower category and I can list those specifically.

Yes. There are several platforms that do almost exactly this—but there’s an important distinction between “AI picks engaging moments” and “AI picks moments based on your actual audience-retention data.”

Best fits

OpusClip is probably the closest to what you're describing. It takes long-form videos, automatically generates multiple shorts, and gives clips an AI Virality Score intended to estimate their potential. Its current tooling also explicitly surfaces high-retention openings. ads.tiktok.com

Riverside is another strong option, particularly for podcasts. Its Magic Clips automatically identifies highlights from transcripts, produces roughly 30–90 second clips, and assigns generated clips a 70–99 score based on how engaging/relevant they are relative to the other clips in that batch. support.riverside.com

But if you mean actual audience retention

That's a more interesting product category.

For example, imagine you upload a 90-minute podcast and the system can say:

“At 37:42, viewers tend to drop—but at 51:18, this topic produces unusually strong retention. Here are 7 potential Shorts, ranked by predicted completion rate.”

Most AI clippers today are predicting engagement from the content itself rather than directly analyzing your historical YouTube/TikTok retention curves. OpusClip's virality score and Riverside's clip score are examples of this distinction. ads.tiktok.comsupport.riverside.com

If you already have YouTube analytics, there's an even more powerful workflow: combine the video's transcript + timestamped retention curve + comments/likes + previous Short performance, then have AI identify the sections most likely to work as new clips.

If you're asking because you're looking to build a platform like this, I can also map out what the ideal product would look like—including the data pipeline from long video → transcript → retention curve → clip candidates → predicted performance → automatically generated Shorts.

Yes. There are several platforms that do exactly this, but there's an important distinction:

• AI-estimated retention (predicts which clips are likely to perform well)
• Actual audience retention (uses real viewer analytics from YouTube or another platform)

No AI can reliably predict actual audience retention before a video is published. However, some tools are very good at identifying strong hooks, emotional moments, and high-engagement segments.

Here are the best options:

1. Opus Clip (my top recommendation)

If your workflow is:

Upload a 60–120 minute video → get 10–30 ready-to-post Shorts

then Opus Clip is probably the best choice.

It will:

• transcribe the entire video
• identify compelling moments
• automatically cut clips
• add captions
• reframe to vertical
• score each clip with a "Virality Score" to help prioritize which ones to publish. opus-clip.com

This is especially good for:

• podcasts
• webinars
• interviews
• conference talks
• educational videos

2. Descript

Descript is more of an editor than an automated clip generator.

It lets you:

• edit by editing the transcript
• search for topics
• use AI to "Find good clips"
• manually polish the results. www.descript.com

Choose this if you still want editorial control.

3. Vizard AI

Vizard sits somewhere between Opus and Descript.

It automatically:

• finds highlights
• creates Shorts
• adds captions
• schedules content

Many creators like it because it requires very little manual work. www.bh-tech-review.com

What if you want clips based on actual audience retention?

That's a harder problem.

The ideal workflow looks like this:

1. Upload the long-form video to YouTube.
2. Wait a few days.
3. Pull YouTube Analytics:
   • audience retention graph
   • spikes
   • rewatches
   • drop-off points
4. Generate Shorts from the segments where viewers stayed engaged.

Very few tools automate this end-to-end because it requires access to your YouTube Analytics. Most AI clip tools estimate engagement rather than using your actual retention data.

If you were building your own system

A powerful workflow would be:

1. Transcribe with a high-quality speech-to-text model (such as Whisper).
2. Use an LLM to identify:
   • hooks
   • stories
   • surprising facts
   • emotional moments
   • quotable sections.
3. Score each candidate clip for:
   • hook strength
   • novelty
   • emotional impact
   • pacing
   • likely retention.
4. After publishing, feed back YouTube Analytics to learn which clips actually retained viewers and refine future selections.

This creates a feedback loop that can outperform one-size-fits-all clipping tools.

If you're thinking about building a product or internal workflow around this, I can also sketch an architecture that combines transcription, LLM analysis, and YouTube Analytics into an automated "content intelligence" pipeline.

Yes. There are several platforms that do exactly this, but there's an important distinction:

• AI-estimated retention (predicts which clips are likely to perform well)
• Actual audience retention (uses real viewer analytics from YouTube or another platform)

No AI can reliably predict actual audience retention before a video is published. However, some tools are very good at identifying strong hooks, emotional moments, and high-engagement segments.

Here are the best options:

1. Opus Clip (my top recommendation)

If your workflow is:

Upload a 60–120 minute video → get 10–30 ready-to-post Shorts

then Opus Clip is probably the best choice.

It will:

• transcribe the entire video
• identify compelling moments
• automatically cut clips
• add captions
• reframe to vertical
• score each clip with a "Virality Score" to help prioritize which ones to publish. opus-clip.com

This is especially good for:

• podcasts
• webinars
• interviews
• conference talks
• educational videos

2. Descript

Descript is more of an editor than an automated clip generator.

It lets you:

• edit by editing the transcript
• search for topics
• use AI to "Find good clips"
• manually polish the results. www.descript.com

Choose this if you still want editorial control.

3. Vizard AI

Vizard sits somewhere between Opus and Descript.

It automatically:

• finds highlights
• creates Shorts
• adds captions
• schedules content

Many creators like it because it requires very little manual work. www.bh-tech-review.com

What if you want clips based on actual audience retention?

That's a harder problem.

The ideal workflow looks like this:

1. Upload the long-form video to YouTube.
2. Wait a few days.
3. Pull YouTube Analytics:
   • audience retention graph
   • spikes
   • rewatches
   • drop-off points
4. Generate Shorts from the segments where viewers stayed engaged.

Very few tools automate this end-to-end because it requires access to your YouTube Analytics. Most AI clip tools estimate engagement rather than using your actual retention data.

If you were building your own system

A powerful workflow would be:

1. Transcribe with a high-quality speech-to-text model (such as Whisper).
2. Use an LLM to identify:
   • hooks
   • stories
   • surprising facts
   • emotional moments
   • quotable sections.
3. Score each candidate clip for:
   • hook strength
   • novelty
   • emotional impact
   • pacing
   • likely retention.
4. After publishing, feed back YouTube Analytics to learn which clips actually retained viewers and refine future selections.

This creates a feedback loop that can outperform one-size-fits-all clipping tools.

If you're thinking about building a product or internal workflow around this, I can also sketch an architecture that combines transcription, LLM analysis, and YouTube Analytics into an automated "content intelligence" pipeline.

Yes. There are several platforms that do exactly this, but there's an important distinction:

• AI-estimated retention (predicts which clips are likely to perform well)
• Actual audience retention (uses real viewer analytics from YouTube or another platform)

No AI can reliably predict actual audience retention before a video is published. However, some tools are very good at identifying strong hooks, emotional moments, and high-engagement segments.

Here are the best options:

1. Opus Clip (my top recommendation)

If your workflow is:

Upload a 60–120 minute video → get 10–30 ready-to-post Shorts

then Opus Clip is probably the best choice.

It will:

• transcribe the entire video
• identify compelling moments
• automatically cut clips
• add captions
• reframe to vertical
• score each clip with a "Virality Score" to help prioritize which ones to publish. opus-clip.com

This is especially good for:

• podcasts
• webinars
• interviews
• conference talks
• educational videos

2. Descript

Descript is more of an editor than an automated clip generator.

It lets you:

• edit by editing the transcript
• search for topics
• use AI to "Find good clips"
• manually polish the results. www.descript.com

Choose this if you still want editorial control.

3. Vizard AI

Vizard sits somewhere between Opus and Descript.

It automatically:

• finds highlights
• creates Shorts
• adds captions
• schedules content

Many creators like it because it requires very little manual work. www.bh-tech-review.com

What if you want clips based on actual audience retention?

That's a harder problem.

The ideal workflow looks like this:

1. Upload the long-form video to YouTube.
2. Wait a few days.
3. Pull YouTube Analytics:
   • audience retention graph
   • spikes
   • rewatches
   • drop-off points
4. Generate Shorts from the segments where viewers stayed engaged.

Very few tools automate this end-to-end because it requires access to your YouTube Analytics. Most AI clip tools estimate engagement rather than using your actual retention data.

If you were building your own system

A powerful workflow would be:

1. Transcribe with a high-quality speech-to-text model (such as Whisper).
2. Use an LLM to identify:
   • hooks
   • stories
   • surprising facts
   • emotional moments
   • quotable sections.
3. Score each candidate clip for:
   • hook strength
   • novelty
   • emotional impact
   • pacing
   • likely retention.
4. After publishing, feed back YouTube Analytics to learn which clips actually retained viewers and refine future selections.

This creates a feedback loop that can outperform one-size-fits-all clipping tools.

If you're thinking about building a product or internal workflow around this, I can also sketch an architecture that combines transcription, LLM analysis, and YouTube Analytics into an automated "content intelligence" pipeline.

Yes. There are several platforms that do exactly this, but there's an important distinction:

• AI-estimated retention (predicts which clips are likely to perform well)
• Actual audience retention (uses real viewer analytics from YouTube or another platform)

No AI can reliably predict actual audience retention before a video is published. However, some tools are very good at identifying strong hooks, emotional moments, and high-engagement segments.

Here are the best options:

1. Opus Clip (my top recommendation)

If your workflow is:

Upload a 60–120 minute video → get 10–30 ready-to-post Shorts

then Opus Clip is probably the best choice.

It will:

• transcribe the entire video
• identify compelling moments
• automatically cut clips
• add captions
• reframe to vertical
• score each clip with a "Virality Score" to help prioritize which ones to publish. opus-clip.com

This is especially good for:

• podcasts
• webinars
• interviews
• conference talks
• educational videos

2. Descript

Descript is more of an editor than an automated clip generator.

It lets you:

• edit by editing the transcript
• search for topics
• use AI to "Find good clips"
• manually polish the results. www.descript.com

Choose this if you still want editorial control.

3. Vizard AI

Vizard sits somewhere between Opus and Descript.

It automatically:

• finds highlights
• creates Shorts
• adds captions
• schedules content

Many creators like it because it requires very little manual work. www.bh-tech-review.com

What if you want clips based on actual audience retention?

That's a harder problem.

The ideal workflow looks like this:

1. Upload the long-form video to YouTube.
2. Wait a few days.
3. Pull YouTube Analytics:
   • audience retention graph
   • spikes
   • rewatches
   • drop-off points
4. Generate Shorts from the segments where viewers stayed engaged.

Very few tools automate this end-to-end because it requires access to your YouTube Analytics. Most AI clip tools estimate engagement rather than using your actual retention data.

If you were building your own system

A powerful workflow would be:

1. Transcribe with a high-quality speech-to-text model (such as Whisper).
2. Use an LLM to identify:
   • hooks
   • stories
   • surprising facts
   • emotional moments
   • quotable sections.
3. Score each candidate clip for:
   • hook strength
   • novelty
   • emotional impact
   • pacing
   • likely retention.
4. After publishing, feed back YouTube Analytics to learn which clips actually retained viewers and refine future selections.

This creates a feedback loop that can outperform one-size-fits-all clipping tools.

If you're thinking about building a product or internal workflow around this, I can also sketch an architecture that combines transcription, LLM analysis, and YouTube Analytics into an automated "content intelligence" pipeline.

Yes. There are several platforms that do exactly this, but there's an important distinction:

• AI-estimated retention (predicts which clips are likely to perform well)
• Actual audience retention (uses real viewer analytics from YouTube or another platform)

No AI can reliably predict actual audience retention before a video is published. However, some tools are very good at identifying strong hooks, emotional moments, and high-engagement segments.

Here are the best options:

1. Opus Clip (my top recommendation)

If your workflow is:

Upload a 60–120 minute video → get 10–30 ready-to-post Shorts

then Opus Clip is probably the best choice.

It will:

• transcribe the entire video
• identify compelling moments
• automatically cut clips
• add captions
• reframe to vertical
• score each clip with a "Virality Score" to help prioritize which ones to publish. opus-clip.com

This is especially good for:

• podcasts
• webinars
• interviews
• conference talks
• educational videos

2. Descript

Descript is more of an editor than an automated clip generator.

It lets you:

• edit by editing the transcript
• search for topics
• use AI to "Find good clips"
• manually polish the results. www.descript.com

Choose this if you still want editorial control.

3. Vizard AI

Vizard sits somewhere between Opus and Descript.

It automatically:

• finds highlights
• creates Shorts
• adds captions
• schedules content

Many creators like it because it requires very little manual work. www.bh-tech-review.com

What if you want clips based on actual audience retention?

That's a harder problem.

The ideal workflow looks like this:

1. Upload the long-form video to YouTube.
2. Wait a few days.
3. Pull YouTube Analytics:
   • audience retention graph
   • spikes
   • rewatches
   • drop-off points
4. Generate Shorts from the segments where viewers stayed engaged.

Very few tools automate this end-to-end because it requires access to your YouTube Analytics. Most AI clip tools estimate engagement rather than using your actual retention data.

If you were building your own system

A powerful workflow would be:

1. Transcribe with a high-quality speech-to-text model (such as Whisper).
2. Use an LLM to identify:
   • hooks
   • stories
   • surprising facts
   • emotional moments
   • quotable sections.
3. Score each candidate clip for:
   • hook strength
   • novelty
   • emotional impact
   • pacing
   • likely retention.
4. After publishing, feed back YouTube Analytics to learn which clips actually retained viewers and refine future selections.

This creates a feedback loop that can outperform one-size-fits-all clipping tools.

If you're thinking about building a product or internal workflow around this, I can also sketch an architecture that combines transcription, LLM analysis, and YouTube Analytics into an automated "content intelligence" pipeline.

Yes. There are several platforms that get close, but there’s an important distinction between “AI predicts which moments will make good shorts” and “AI uses your actual audience-retention data to decide what to clip.”

Best fits

1. OpusClip — probably the closest out-of-the-box

• Upload a long video and it transcribes/analyzes it automatically.
• Finds multiple potential short-form clips.
• Gives clips a Virality Score to rank their likely performance.
• Handles captions, reframing, B-roll, etc.
• It can generate 20+ clips from an hour of source video. www.opus.prowww.opus.pro
• The catch: its scoring is primarily AI prediction, not “your specific audience watched this timestamp longer.”

2. Descript — best if transcript analysis matters

• Transcribes long videos and lets you edit through the transcript.
• Its AI assistant can identify “viral-worthy” moments and turn them into clips.
• Better suited if you want to review/edit the AI's choices rather than completely automate the process. www.descript.com

3. Riverside — good all-in-one workflow

• Automatic transcription.
• AI “Magic Clips” finds highlights from long recordings.
• Particularly useful if you're also recording podcasts/interviews on the platform. riverside.fm

But if you mean actual audience retention

This is the more interesting product idea.

Imagine feeding it:

90-minute podcast → transcript + viewer-retention curve + comments + previous clip performance

Then it could identify things like:

• 12:43–13:18: unusually high retention → strong candidate
• 27:05–27:42: viewers repeatedly rewatched this section → very strong candidate
• 43:10: 18% drop-off → don't start a clip here
• 56:20–57:05: controversial statement + high comments → candidate for a debate clip
• 1:12:30: strong retention but weak standalone context → needs a better hook

That's different from most AI clipping tools today. The really powerful version would close the loop: generate clips → publish → ingest retention/watch-time data → learn what your particular audience responds to → improve the next batch.

OpusClip is moving in that direction with personalized analytics, but its current positioning still centers heavily on AI virality scoring rather than feeding your channel's historical retention curve directly into clip selection. www.opus.prowww.opus.pro

If you're asking because you're considering building something like this, I think there's a meaningful gap between existing “AI clip generators” and a true retention-driven content repurposing platform. I can map out what that product would look like—including the data pipeline from YouTube retention → transcript → candidate clips → scoring → automatic publishing.

Yes. There are several platforms that do almost exactly this, although there’s an important distinction between “AI predicts which moments will perform well” and “AI uses your actual audience-retention data to choose clips.”

Best fits

• OpusClip — probably the closest match. You upload a long video and it identifies short-form moments, automatically edits/reframes/captions them, and ranks clips with a Virality Score based on factors such as hook, flow, value, and trend relevance. Its newer scoring system is trained against real social-video performance data. help.opus.pro
• Vizard — particularly good if you have podcasts, interviews, webinars, lectures, etc. It transcribes the entire video, detects high-retention/engaging sections, and can generate multiple Shorts/Reels/TikToks automatically. You can also search the transcript and turn selected sections into clips. vizard.ai
• Vizard AI Highlights — worth looking at specifically for your use case because it analyzes speech patterns, pacing, emotional cues, and narrative structure to identify potential high-performing moments. vizard.ai

But if you mean actual audience-retention data

That's a more interesting product category.

For example, imagine you upload a 60-minute YouTube video and the system has access to its analytics:

0:00–2:10 — 82% retention
2:10–7:30 — 61%
7:30–12:00 — 38%
12:00–15:20 — 74%
15:20–22:00 — 41%

A really sophisticated system could combine that with the transcript and say:

“These 7 moments are your best candidates for Shorts because viewers disproportionately stayed through them.”

Then it could produce:

• the 20–45 sec clip
• a stronger opening hook
• captions
• suggested title
• predicted performance score
• the original timestamp
• why it thinks the segment will work

That's different from what OpusClip/Vizard primarily do today. Their “retention”/“virality” assessment is largely an AI prediction of what should perform well, rather than simply saying “this exact timestamp had 91% actual viewer retention in your YouTube analytics.” OpusClip's scoring, for example, is based on characteristics of the clip and models trained on historical social performance. help.opus.pro

If you're thinking about building this

There is actually a pretty compelling product opportunity here:

Long video → transcript → retention curve → identify peaks → generate clips → learn from actual clip performance.

The feedback loop is the key. Instead of generic “AI thinks this is viral,” the platform could learn:

your audience + your content + your historical retention + your actual Shorts performance.

That could become substantially better at clip selection over time.

If that's what you're exploring, I can also map out the existing competitors and exactly what you'd need to build a “retention-aware OpusClip”, including YouTube API/analytics, transcription, clip detection, scoring, and the likely tech stack.

Yes. OpusClip is probably the closest match to what you’re describing.

OpusClip can take a long-form video, transcribe/analyze it, identify moments it thinks are likely to perform well, and automatically turn them into short vertical clips. It gives clips a Virality Score and ranks the generated clips, rather than simply chopping the video at fixed intervals. opus-clip.com

The important distinction: “audience retention”

There are actually two different things you might mean:

1. Predict retention before publishing

Tools like OpusClip analyze the content, hooks, pacing, sentiment, etc. and predict which moments are likely to hold attention. OpusClip specifically says it surfaces high-retention openings and scores clips for viral potential. www.opus.pro

2. Use your actual audience-retention data

This is more interesting if you already have a YouTube channel. Ideally, the platform would look at your existing videos' retention curves, identify where your audience tends to stay engaged, then find similar moments in new long videos and recommend clips based on your own audience—not generic viral patterns.

OpusClip is stronger at #1 than #2. Its current analytics can track performance and provide optimization suggestions, but its own documentation describes some personalized analytics functionality as still developing. opus-clip.comwww.opus.pro

Other good option

Riverside is worth considering if you're doing podcasts/interviews. It provides AI transcription, lets you edit the video through the transcript, and has “Magic Clips” that automatically identify and extract good moments from long recordings. riverside.fm

If your goal is specifically:

Upload a 1–2 hour video → transcribe it → analyze the conversation → find the 10–20 strongest moments → rank them by predicted retention → generate Shorts/Reels/TikToks

I'd start with OpusClip.

If you mean:

Analyze my actual YouTube retention data and use it to learn what my specific audience responds to, then automatically find similar moments in my future videos

that's a more specialized workflow, and I can point you toward platforms that do that—or explain how to build it yourself using YouTube Analytics + transcription + an AI clipping tool.

Yes. There are several platforms that do long-video transcription → identify compelling moments → generate short clips, but there’s an important distinction:

Most tools predict retention/virality from the content itself. Fewer actually use your historical audience-retention data from YouTube/TikTok/etc. to decide what to clip.

Best options

1. OpusClip — probably the closest match
   • Upload a podcast, interview, webinar, YouTube video, etc.
   • AI transcribes and identifies potential short-form segments.
   • Generates multiple clips automatically.
   • Gives each clip a 0–99 Virality Score, based on things like hook, flow, value, emotional resonance, and trends. help.opus.pro
   • Has auto-captions, reframing, B-roll, and publishing tools.

Caveat: its score is an AI prediction of performance, not your actual historical viewer-retention curve.

2. Vizard — excellent for transcript-driven clipping
   • Automatically transcribes long videos and finds highlights.
   • Specifically looks for emotional moments, storytelling, pacing, and high-retention segments.
   • Can generate multiple Shorts/Reels/TikToks from one long video. vizard.aihelp.opus.pro
   • It also supports searching a long video by natural-language prompts—for example, finding the section about a particular topic in a 3-hour recording. help.vizard.ai
   • Its API can handle videos up to 600 minutes / 10 GB, which makes it interesting if you're building a workflow rather than just editing manually. docs.vizard.ai

The workflow I'd use

If your goal is:

"Take a 60–180 minute video, understand what was said, predict which moments will hold attention, and give me 10–20 Shorts worth testing."

I'd start with OpusClip.

But if what you really mean is:

"Look at my YouTube Analytics retention data and discover that viewers tend to drop at 42:15 but stay engaged from 57:30–58:40, then automatically turn the high-retention sections into Shorts."

That's a more sophisticated product. OpusClip/Vizard don't appear to simply ingest your channel's actual retention curves and use them as the clipping algorithm. Their current approach is primarily AI/content-based prediction. vizard.aihelp.opus.pro

If you're asking because you're thinking about building a platform like this, there's actually a pretty interesting gap here: combining transcript + semantic hooks + actual historical retention curves + comments/engagement + short-form performance could produce substantially better clip recommendations than generic "viral moment" detection.

Yes. There are several platforms that do almost exactly this, but there’s an important distinction between “AI guesses which moments will perform well” and “AI uses your actual audience-retention data to find clips.”

Best options

OpusClip is probably the closest match to what you're describing. It analyzes an entire long-form video, identifies moments it considers likely to capture/hold attention, then ranks the resulting clips with a “Virality Score.” Its current research product also analyzes large volumes of short-form videos to identify hook and length patterns. opus-clip.com

Riverside is particularly good if your source material is podcasts, interviews, webinars, or recorded conversations. Its Magic Clips automatically identifies highlights, generally producing 30–90 second clips, and its scoring explains how engaging/relevant each candidate is relative to the others. support.riverside.com

But if you mean actual audience retention data…

That's a more interesting problem.

For example, imagine you have a 90-minute YouTube podcast and YouTube Analytics tells you:

00:07:32 — retention spike
00:24:18 — retention spike
00:47:05 — 72% of viewers still watching
01:02:40 — major drop-off

What you really want is a system that combines:

transcript + video understanding + existing retention curve → candidate clips → predicted short-form performance

Most AI clipping platforms today are primarily doing the prediction side rather than feeding your video's actual historical viewer-retention graph directly into clip selection. OpusClip, for example, describes its scoring as identifying segments likely to catch and hold attention rather than saying it uses your YouTube Analytics retention curve. opus-clip.com

So if you're looking for something specifically for a YouTube creator/podcast workflow, I'd rank them:

1. OpusClip — best automated “give me 10 shorts from this 2-hour video.”
2. Riverside — best if you're already recording podcasts/interviews there.
3. Munch — strong if you want the clips plus lots of downstream social content. It can work directly from a YouTube/Vimeo URL or transcript. munch.video
4. Descript — better when you want AI assistance but still want editorial control.

If you're asking because you're thinking about building a platform that does this, there's actually a pretty interesting gap here: “turn my long-form content + historical audience-retention data into a ranked library of clips with predicted retention.” That's substantially more differentiated than another generic AI clip generator.