Workflow guide · AI Media Ops
AI highlights and clipping for media teams
Clipping is usually the first workflow a media company hands to AI, because the work is repetitive, the output is easy to judge, and every platform wants more of it. Here is how to set it up so it ships clips people watch instead of clips people skip.
What the job actually is
"Make clips" hides five separate tasks. Most failed rollouts automate the first one and leave the other four to the same overloaded editor.
- Find the moment. Scan the full episode, game or interview for the segments worth cutting.
- Cut and reframe. Trim to a clean in and out point and reframe 16:9 to 9:16 so the speaker or the play stays in frame.
- Caption and brand. Burned-in captions, lower thirds and the show's look.
- Package. Title, hook line, description and hashtags for each platform.
- Clear and publish. Check rights and brand safety, then schedule to each platform.
Buy, build or both
There are three ways to cover those five tasks. Most teams end up mixing them.
| Approach | Fits when | Watch out for |
|---|---|---|
| Creator clipping tools (Opus Clip, Descript, CapCut) | Talk-led shows, podcasts and interviews where the moment is in the transcript | Per-seat and per-minute pricing at volume; weak on sports and visual moments; brand templates are limited |
| Enterprise highlight platforms (for example WSC Sports for sports) | Live sports or high-volume news with structured data feeds | Contract size and setup time; built for one content type |
| An agent pipeline you own | A back catalog or a mixed slate, where you want your own rules for what counts as a moment | Needs an owner and a review gate; quality drifts without written rules |
The common pattern is to buy the cutting and reframing, and own the judgment: what counts as a moment for your brand, what never gets clipped, and how each platform's packaging should read.
The agent pipeline, step by step
- Transcribe everything first. A Whisper-class model on your own hardware handles a back catalog for little more than hardware and electricity. The transcript is the index every other step uses.
- Score candidate moments against a written rubric. A strong hook in the first three seconds, a complete thought, an emotional peak or a clear takeaway, and no dependence on context from earlier in the show.
- Cut with the tool, not the model. Hand timecodes to the clipping tool or an ffmpeg script. Language models pick moments; they should not be trusted to cut frames.
- Package per platform. The same clip gets a different first line on TikTok, YouTube Shorts and Instagram Reels.
- Gate before publish. A reviewer agent checks the rubric, then a human approves the batch. One approval per batch, not per clip, or the human becomes the bottleneck again.
Rights and brand safety come before speed
Clips travel further than the show they came from, so the rights problems travel too.
- Licensed music, third-party footage and guest appearances may be cleared for the broadcast but not for social clips. Flag them in the transcript step and exclude them automatically.
- Keep a "never clip" list: minors, sensitive segments, anything under legal review.
- If you qualify, register your source episodes with YouTube Content ID or Meta Rights Manager so re-uploads of your work earn for you instead of the uploader.
How to tell it's working
Count clips that hold attention, not clips posted. Track the share of each clip that viewers watch, saves and shares per view, and first-pass approval: the share of clips the human approves without changes. When first-pass approval is low, the fix is in the rubric, not the model.
Price it before you build it. Enter your team's weekly clipping hours in the AI Ops ROI calculator to see what automating it is worth. If the number justifies it, the AI Ops diagnostic maps your pipeline and the first agent team.