ChatGPT answers, unedited

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. 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. 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 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.