Visual QA

Quality assurance for AI at scale

Even the best AI models get it wrong, so Visual QA scores every output for fidelity, catches the misses, and corrects the details before they reach your customers.

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How Visual QA works

The intelligence layer that analyzes your visuals before and after they're generated

Before a visual is created, Visual QA understands every image so the correct generations are applied. Post-generation, it rates the outputs and corrects the details a model got wrong.

Step 1

Analyze the inputs

Visual QA understands what each image is, ensuring it is processed with the correct requirements or removed from the workflow.

Step 2

Rate the outputs

Every generation is scored against fidelity models built for your vertical, and when the quality bar is reached, the version closest to the real product is selected.

Step 3

Retry loop

Where a model gets a detail wrong, Visual QA analyzes what caused the miss and processes another generation fixing the prompt.

Step 4

Publish

Only generations with 100% fidelity make it through the entire quality assurance flow to ensure all images meet your platform requirements.

Catch the problems before your buyers do

Visual QA focuses on product fidelity, the details that would make a visual misrepresent the real product. It scores for the things a buyer would spot and a model can get wrong, such as color, shape, textures, patterns, and unwanted visual changes.

Stop bad inputs and route each image to the right AI workflow

Check source images for content, safety, image quality and composition before generation, so unsuitable inputs don’t enter the wrong workflow or create avoidable failures. Visual QA then categorizes each image and routes it to the workflow that fits.

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Catch product changes before they reach your catalog

Fidelity Raters compare generated outputs against the original image and score the product details that matter, so subtle inaccuracies are caught before buyers see them.

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Is Visual QA right for your team?

Visual QA fits in for businesses attempting to utilize AI at scale across hundreds of thousands or even millions of images. This is when the problem of fidelity becomes a revenue problem.

Teams tired of AI hallucinations

All images are not created equally. They vary in category, type, shot, style & specification. If you currently struggle to sort through images at scale and can’t apply custom AI logic at the image-level then Visual QA could solve your challenges.

Teams replacing a manual review queue

If you add humans as a final step to QA merchant or supplier images by hand today, Visual QA automates the check so your team stops reviewing image by image introducing huge cost saving.

Teams that built their own scoring

If you already built in-house QA models, but aren’t satisfied with the results because you don’t have access to verticalised benchmark data, Visual QA can replace or extend them, with rating models trained on a scale most teams can't build alone.

Visual QA and the Enterprise Guarantee

Visual QA is the intelligence layer inside Photoroom Visual Agents, the suite that analyzes, edits, and quality-checks every visual across your production loop. The Enterprise Guarantee builds on that layer to back your accepted outputs as a contracted service. Start with Visual QA to score and fix at scale, and add the Enterprise Guarantee when you want the outcome committed in your contract.

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Visual QA

Frequently asked questions

What does Visual QA check for?

It scores product fidelity: the details that would make a visual misrepresent the real product, such as color, shape, textures, patterns, and visible artifacts. It's a fidelity check against your vertical, not a generic image-quality filter. It ensures AI doesn’t hallucinate and change your product.

How is this different from the image model's own output?

AI models process an input and produce an output. It doesn't analyze the input and reason through the generation to improve the output. It also doesn’t measure whether that visual is true to your product, or fix it when it isn't. Visual Quality Assurance adds the layer that scores every output for fidelity and corrects the misses.

Which categories does rating support today?

Rating is available for food and fashion at launch, with verticalized models for each. Analyze is available to all enterprise customers.

Is Visual QA available through the API?

Analyze, Generate & Rate are available through the Visual QA endpoint. Fix is available today in the web app and in batch, with API access in Beta testing with some customers, please reach out if you would like to be included in Beta testing.

Can it work with our existing in-house scoring?

All scoring is customisable to business-specific requirements all powered by industry benchmark data. Whatever logic forms the bar a generation needs to hit, we can tailor it to your business.