The pitch for AI video tools is zero-touch production. Upload the video, get the clips, send to client. In practice, sending AI output directly to clients without a QA pass is one of the fastest ways to lose trust you spent months building.
This isn't an argument against AI — it's an argument for knowing exactly what to check before anything leaves your workflow. A 15-minute QA pass catches 90 percent of what clients would otherwise flag. Here's the checklist we've seen work at agencies running 30-plus clients monthly.
Why AI output fails without QA
AI video tools fail in predictable ways. The failures aren't random — once you've seen enough output you can anticipate exactly where things will go wrong. That predictability is actually good news: you can build a checklist around it.
The most common failure modes:
- Clips that start or end mid-sentence — the AI found a high-energy moment but cut before the thought completed
- Speaker out of frame — especially in multi-camera footage where the AI reframes toward the wrong speaker
- Caption errors on proper nouns, technical terms, and brand names — transcription models handle common words well, specialist vocabulary poorly
- Clips that are technically correct but contextually wrong — the sentence sounds viral but was sarcastic or hypothetical in context
- Wrong aspect ratio applied — common when client configs aren't stored or the tool defaulted to its last setting
The 5-point QA checklist
Run this on every batch before client delivery. Experienced reviewers do this in 8–15 minutes per 10 clips once they know what to look for.
Complete thought check
Watch the first and last 3 seconds of every clip. Does it start with context? Does it end on a complete thought? A clip that opens with "— and that's why I think" or ends with "so the real question is—" failed this check.
Frame and reframe check
Is the speaker's face fully in frame for the duration? Any awkward zoom jumps or tracking errors? Pay extra attention to clips from multi-person recordings — AI reframing struggles most here.
Caption accuracy check
Read every caption line on at least 3 randomly selected clips per batch. Check the client's brand name, product names, and any technical terms they use repeatedly. One wrong caption on a client's own company name is unforgivable.
Format and branding check
Does the aspect ratio match what this client's configuration specifies? Is the caption font, color, and position correct? If the client has a logo watermark, is it in the right position? Cross-reference against the client config doc, not your memory.
Context and tone check
Watch the clip cold — pretend you've never seen the source video. Does it make sense as a standalone piece? Is the tone appropriate? The AI selected this because it looked like a viral moment. You're checking whether it actually is one.
Internal QA vs client review: where each one lives
Internal QA and client review are not the same thing. Internal QA is your process — the five points above — and nothing should reach the client until it passes. Client review is about preference, brand judgment, and context you don't have.
Conflating the two is where agencies get buried in revision rounds. When a client flags a caption error or a badly framed clip, that's not a revision — that's an internal QA failure that reached the wrong stage. Revision rounds should only start after you've removed everything you could have caught yourself.
Practically: set a target of zero technical errors reaching client review. Caption mistakes, wrong formats, bad frames — none of these should appear in what clients see. What clients review is content judgment: which clips to post, whether the energy is right for their brand, which moment to lead with.
Time targets that actually work
QA should take roughly 90 seconds per clip for an experienced reviewer. That's 15 minutes for a 10-clip batch, 30 minutes for 20 clips. If your reviewer is taking longer, one of three things is happening:
- They're re-editing clips instead of flagging them for the editor (separate those roles)
- The AI output quality is too low and you're screening bad material (fix the tool or the source footage requirements)
- They don't have a client config doc to reference and are working from memory (fix this immediately)
QA time above 3 minutes per clip is a process problem, not a headcount problem.
Building QA into your production schedule
QA should be a scheduled step with time blocked for it — not something that happens whenever the editor finishes. "I'll QA it before I send it" is how QA gets skipped at 5pm on a Friday when the client is waiting.
Production schedule that works: editors finish clips by Wednesday midday. QA runs Wednesday afternoon. Client delivery goes out Thursday morning. Client has until Friday to respond. This is a rhythm, not a suggestion — every client is on this schedule from day one.
Common questions
How long should QA take for AI-generated video clips?
About 90 seconds per clip for an experienced reviewer with a clear checklist. A 10-clip batch should take 12–15 minutes. Significantly longer usually means the process or the AI output quality needs attention.
What are the most common AI video clipping errors to check for?
Mid-sentence cuts, speaker out of frame, caption errors on brand names and technical terms, wrong aspect ratio, and clips that are accurate but contextually wrong (sarcastic or hypothetical statements that read as sincere).
Should clients be involved in QA?
No. Clients review for preference and brand judgment — not technical correctness. Technical errors should be caught internally before client review starts. Mixing the two stages is why revision cycles get expensive.
Scale your short-form without the babysitting
Skapo stores client configurations — aspect ratios, caption styles, branding — and applies them automatically every batch. Fewer QA failures before you even open the checklist.
Try it freePosted by the Skapo team — June 2026