Video Intelligence
AI Video QA
Every video is checked against your brand, caption and accessibility checklist before it publishes, not after.
Video mistakes are expensive to fix once they are live: a wrong logo, captions that drift from the audio, on-screen text nobody can read on a phone. AI Video QA checks each video against the checklist you set before it publishes. You get a pass or fail on every item, with timestamps, and a person on your team still makes the final call.
- Caption errors and timing drift caught before a video goes live
- A pass or fail report with timestamps for every checklist item
- Brand and accessibility rules checked the same way on every video
$3,400
20–30 working days
Optional maintenance $249/mo
What it is
AI Video QA reviews video against a checklist you set, covering brand elements, caption accuracy and accessibility, and reports what passes and what needs fixing before the video goes live. It is a gate before publishing, not a tool for making the video itself.
Why businesses buy it
Today, video usually gets checked by whoever has a spare ten minutes, and the checklist lives in someone's head. Errors slip through, customers spot them, and the video has to be pulled, fixed and posted again. After this build, every video meets the same written checklist before it publishes, and your reviewers only look at what failed. Because the checklist, the destination and the review step are written on the Scope Card before you order, you know exactly what gets checked and where the reports land.
Problems this solves
Videos go live with missing logos or wrong brand elements
Each video is reviewed against the checklist you set, including brand elements. A report shows what passed and what needs fixing before the video is published.
Caption mistakes are noticed only after viewers point them out
Caption accuracy is part of the check. Errors are flagged in the report before publishing, so they can be fixed while the video is still private.
Accessibility checks get skipped when deadlines are tight
Accessibility items on your checklist are checked on every video, not just when someone has time. The gate runs the same way each and every time.
Manual review is inconsistent between different reviewers
The QA step applies one checklist to every video. Reviewers start from a clear pass-and-fix report rather than their own memory of the house rules.
A good fit if
Video goes out under your brand from more than one person, and mistakes only get caught after it's already live.
You have a checklist in your head, such as logo placement, caption accuracy or contrast, that never gets checked consistently.
Accessibility requirements, such as caption timing or contrast, matter for the channels you publish to.
You want a written pass or fail report on each video before it's approved to publish, not just a gut check.
Not the right fit if
You need captions generated in the first place, not checked. See AI Captioning, which produces the captions this build can then review.
You need to search across a video library for a specific moment, not review one video before publishing. See AI Video Search.
You need notes and action items pulled from a recording rather than a pass or fail check against a checklist. See AI Video Knowledge Extraction.
You need still images and ad creative checked, not video. See AI Creative QA, the image version of this check.
You need review against more than one checklist for different channels. Add a note at checkout, or use our Custom AI enquiry form for that scope.
How it works
- Your checklist is defined. Brand rules, caption accuracy standards and accessibility requirements are agreed with you and turned into the criteria the review runs against.
- A video is submitted for review. It's sent to the one destination we agree, before it's approved to publish anywhere.
- Checks run against the video. Captions are compared against the spoken audio, brand elements such as colors, logo and intro or outro are checked, and accessibility items like contrast and caption timing are reviewed.
- Issues are flagged with timestamps. A report lists each checklist item as pass or fail, pointing to exactly where in the video an issue occurs.
- A person confirms the flagged issues before the report is released, so borderline calls get a human read.
- The pass or fail report is delivered to your destination, so the video can be approved or sent back for fixes before it publishes.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Your checklist, or the brand and accessibility rules you want every video checked against.
- Ten to twenty example videos, including some with known issues, so the checks are calibrated correctly.
- Your brand guidelines, such as approved colors, logo placement and intro or outro requirements.
- Access to the one destination where videos will be submitted for review.
- The name of the person who approves or overturns a flagged issue.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- Your business information (company basics, hours and brand voice), shared during intake, and one decision-maker who can approve outputs.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake verified, checklist and destination agreed.
Weeks 1–2
Review checks built and calibrated against your example videos.
Week 3
Destination system connected and the report formats configured.
Weeks 3–4
Twenty-five sample reviews run and sent to you for review.
Week 4
Your review and two rounds of changes to the checklist or thresholds.
Go-live
Live, recorded handoff session, support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $3,400 |
| Express | 15–25 working days | $4,000 |
| Rush | 10–20 working days | $4,600 |
What you receive at handover
- A video QA gate that checks videos against your checklist before they go live
- Your checklist for brand elements, caption accuracy and accessibility, set in the guardrail configuration
- Pass-and-fix reports delivered to one destination (fixing the video stays with your team)
- A test report on sample QA results you reviewed and approved
- An admin guide, a recorded handoff session and 30 days of post-launch support
Expected benefits and how to measure them
- Fewer errors can reach your audience.
- Review can follow the same standard every time.
- Reviewers can spend their time on fixes rather than hunting for problems.
- Issues caught before publishing
- Count failed checks in the QA reports each month.
- Corrections after publishing
- Track videos edited or pulled after release, compared with the months before launch.
- Review time per video
- Log reviewer time per video before launch and after.
Before you start: Record how many published videos needed fixes in recent months and why. Note how long manual review takes now.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is one-time. Running it is paid in your own accounts, mostly AI review usage: roughly $20 to $150 a month, depending on how many videos go through the checklist. A team reviewing a few videos a week sits toward the lower end.
AI usage during the build and testing is also billed to your accounts, so you keep ownership of every provider relationship. Nothing runs through us.
Ask about running costsCustom requirements
If you need a second checklist for a different channel, more than three output formats, or reviews against more than one destination, add a 'Custom requirements' note at checkout. You get a written Change Quote, and that work only starts once you accept it. Larger or bespoke projects use the Custom AI request form instead.
Request a custom versionKeep it running
Monthly maintenance covers monitoring, fixes, updates, a monthly health report and change hours to update the checklist as brand or accessibility rules change. It starts after the thirty days of support, bills monthly, and can be cancelled any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A brand-compliance tool charges a monthly seat fee per reviewer | A QA reviewer is a full hire, an ongoing salary | Ours is one fixed price, shown on this page before you buy |
| Consistency | A compliance tool needs someone to set up rules and keep tuning them | A reviewer's checks vary with attention and workload | Ours applies the same checklist to every video, every time |
| Delivery | A compliance tool is ready quickly but needs manual rule setup | Hiring a reviewer takes weeks or months | Ours has a fixed delivery time shown before you buy, with faster options |
| Output | A compliance tool often just flags a generic score | A reviewer's notes vary in detail | Ours reports pass or fail per checklist item, with timestamps |
Questions about this product
What does AI Video QA actually check on a video?
It checks against the checklist you define during intake, typically covering caption accuracy against the spoken audio, brand elements such as logo, colors and intro or outro, and accessibility items like contrast and caption timing. Each item comes back as a pass or fail, with a timestamp pointing to any issue found.
Does AI Video QA create or edit the video itself?
No. It is a review gate, not a production tool: it reports what passes and what fails against your checklist, but it does not generate captions, footage or edits. If you also need captions produced, AI Captioning, a separate build, handles that step.
How is AI Video QA different from AI Video Knowledge Extraction?
They do different jobs. AI Video Knowledge Extraction pulls decisions, action items and key points out of a recording, which suits meetings, webinars and training sessions. AI Video QA reviews a finished video against a pass or fail checklist covering brand, captions and accessibility before it publishes. If you need both, they can run side by side, and each has its own page.
How much does AI Video QA cost to run each month?
Running it is paid directly to your AI review provider, in your own account, and usually sits between $20 and $150 a month depending on how many videos you check and how long they are. We charge nothing per video or per review. The one-time build price, with each delivery speed, is shown in the panel on this page.
Can AI Video QA catch caption timing and accessibility issues?
Yes, when those items are part of the checklist you set during intake. It compares captions against the spoken audio for timing and accuracy, and checks accessibility factors such as contrast where relevant, flagging anything that fails with a timestamp so it can be fixed before publishing.
What do I need to provide before AI Video QA starts?
Your checklist or the brand and accessibility rules to check against, ten to twenty example videos including some with known issues, and access to the destination where videos will be submitted. Account access is shared by adding our team as a user or creating a key you control, never by email, and one approver signs off flagged issues.
Can the checklist change after AI Video QA is live?
Yes. Updates to the checklist, such as a new brand rule or an updated accessibility requirement, fit inside the monthly change hours in maintenance. A second checklist for a different channel is a larger change, quoted through the Custom requirements route.
Who reviews the issues AI Video QA flags?
A person reviews the flagged issues before the report is released, so a borderline call still gets a human read rather than an automatic reject. The named approver from your team then decides whether a flagged video is fixed or published as is.
Stop finding brand mistakes after the video is already live.
Order AI Video QA and it goes live on a fixed delivery date, checking every video against your checklist before it publishes.
Works well with
Video Intelligence
AI Video Knowledge Extraction
Extracts key points, steps and quotes from video libraries.