Your Pre-Launch AI Avatar Testing Checklist
Everything that should be verified before your AI avatar goes live — organized by the order you should check it, not the order that feels most natural.
I run the creative side of our agency and I've developed a pre-launch checklist for AI avatars after watching too many good generations get deployed badly. The checklist exists because the order in which you check things matters — some checks only surface problems that downstream checks depend on, and if you run them out of sequence, you can clear the later checks and still have a broken deployment. The sequence below is intentional: each step assumes the previous ones have already passed.
This checklist is not about perfection — it's about catching the category of problems that are predictable, fixable before launch, and invisible during generation but obvious after deployment. Each item takes less than five minutes. The whole checklist takes about an hour. That hour is far cheaper than discovering a problem two weeks into a live deployment.
Technical Checks (Run First)
Before anything else, verify the technical fundamentals. These checks are binary — pass or fail — and any failure here makes the rest of the checklist moot.
Resolution: minimum 1000px on the shortest dimension for any professional use case; 2000px+ for print or large-format
File format: PNG for transparent backgrounds, JPEG at quality 85+ or WebP for web use
Crop safety: place the image in a circle crop (used by most platforms) — does the face sit well within it?
Scale test: view at 80px, 150px, 400px, and full resolution — the face should read clearly at all four sizes
Artifact check: zoom to 200% — are there generation artifacts at the hairline, ears, or collar? These are invisible at normal size and distracting to people who look closely
Identity Checks (Run Second)
After the technical checks pass, verify that the avatar actually represents the person accurately.
Side-by-side with a recent photograph: do the key features match? Jawline, nose bridge, eye color, hair texture
Expression check: does the avatar's expression match the intended register? If it was supposed to be confident and engaged, is that what it reads as?
Age accuracy: does the avatar represent the person's current age, not an idealized younger version?
Third-party check: show the avatar to someone who knows the subject — ask if they'd recognize them from this image alone
Brand Checks (Run Third)
With identity confirmed, verify brand alignment.
Wardrobe aligns with brand tone (formal/casual, color palette, professional category)
Background is consistent with the visual language of the deployment context
Color temperature of the image is compatible with the brand's color palette
Place the avatar on the actual page or profile where it will live — does it integrate, or does it float?
How Kyndrify Prevents Checklist Failures Upstream
Many of the items on this checklist are problems you shouldn't be catching at the checklist stage — they should have been prevented by a good generation process. Artifact issues, scale failures, and brand misalignment are usually symptoms of a generation that wasn't structured carefully enough. Kyndrify reduces the rate of checklist failures by encoding the parameters that prevent these issues into the generation framework. When the platform's button-based system is guiding the generation, the floor of output quality is higher and checklist failures are less frequent. The checklist still matters — no process eliminates all errors — but the errors it catches are less common and less severe.
Run the checklist in sequence, every time, before every deployment. It takes an hour. The alternative is discovering problems after launch, which takes much longer to fix and leaves a window where your avatar is communicating something wrong. There's no version of this where skipping the checklist is the better choice.
Sources
TTGC / Kyndrify — patterns from building AI avatar tooling.
Smashing Magazine — industry standards for profile image specifications. smashingmagazine.com
