AI video quality control that catches what a prompt can't
AI video quality control pairs automated checks with human review on every delivery — hallucination, brand safety, label accuracy and colour match, caught before a video ships, not after a customer flags it.
- Human reviewon every delivery
- Multi-pointQC checklist
- Revision loopbuilt into SLA
- Artefact scan
- Label accuracy
- Colour match
- Hallucination check
- Brand safety
What is AI video quality control?
An AI video quality control process is the review layer that sits between AI-generated footage and delivery — checking for hallucination (details a model invented that don't match the real product), brand safety, label and packaging accuracy, colour match to the actual product, and visual artefacts. Because generative video can render a label wrong or a detail that never existed, this isn't optional polish — it's what makes AI-generated product video safe to publish at all.
- Artefact detection
- Label accuracy
- Colour match
- Documented QC record
Cleared to ship
or back into the revision loop
A checklist built for what AI video gets wrong
Every delivery passes the same checklist before it reaches your approval stage, not a spot-check on a sample.
Hallucination check
Frame-by-frame review for details the model invented — text, logos, or product features that don't match the real product.
Label & colour accuracy
Packaging text, claims and colour are checked against your actual product, not just visually plausible.
Brand safety & artefact detection
Flagged for anything off-brand or visually broken before it reaches your review, not after.
How every delivery gets checked
- 01
Automated first pass
Every render runs through automated checks for common generative artefacts and inconsistencies.
- 02
Human review against your product
A reviewer checks label accuracy, colour match and brand safety against your actual product and guidelines.
- 03
Sign-off, then delivery
Only videos that clear the full checklist are marked ready and delivered for your approval.
What every delivery is checked against
Hallucination check
Invented text, logos or features flagged and corrected
Label accuracy
Packaging text and claims matched to your actual product
Colour match
Product colour checked against reference images
Artefact detection
Visual glitches and generative inconsistencies flagged
Revision loop
Included in every delivery SLA, not billed as an extra
Sign-off record
A documented QC pass accompanies every delivered file
Where QC applies across our video
PillarAI Product Video
The full AI production service this quality-control layer runs inside for every delivery.
See the full service→
ClusterAI UGC Videos
Synthetic creator content where label accuracy and disclosure checks matter most.
Explore AI UGC→
PillarAmazon Product Video Services
Where label and colour accuracy directly affects listing compliance.
Explore Amazon video→AI video quality control, answered
It's when a generative model renders a detail that isn't real — invented text on packaging, a feature the product doesn't have, or a logo that doesn't match. Our QC checklist specifically looks for this before delivery.
Built into every project. Human review and the revision loop it enables are part of the standard delivery SLA, not an add-on line item.
Both. An automated first pass catches common artefacts, and a human reviewer checks label accuracy, colour match and brand safety against your actual product and guidelines.
It goes back into a revision loop before it ever reaches your approval stage — you only see videos that have already cleared our internal checklist.
Yes, brand-specific checks — a compliance claim, a specific colour standard, a required disclosure — can be added to the checklist at brief stage.
Yes, a documented sign-off accompanies every delivered file, so your team has a record of what was checked.
See our QC process
Ask us to walk through the checklist on a sample delivery. We'll show you exactly what gets checked before anything ships.