Apply image analysis, content moderation, age assurance and automated workflows to digital commerce and customer interactions.
Where this stands: Moderation, age assurance and seller verification are available to retailers and marketplaces today.
Sample refund claimOrder 58-2290, table lamp
- No signs of AI generationSeparate signal
- No localized editsSeparate signal
Every listing, review and refund is a claim about the world.
A marketplace depends on what sellers say about themselves and their products, and what buyers say back. Product photos, seller documents, reviews and the photos sent with a refund request all shape decisions that happen at scale.
Generated images make it cheap to flood a catalogue with listings that do not exist, or to fake damage on a delivered item. Age-restricted products need checks that do not drive honest customers away at checkout.
What retail teams answer to
Knowing your traders
Under the EU Digital Services Act, online marketplaces must collect and check information about the traders who sell on them.
Age-restricted sales
Alcohol, tobacco, knives and other restricted goods need an age check online that is proportionate to the product.
Refund and listing abuse
Edited or generated photos support false refund claims and fake listings, and the cost lands on margins and on buyer trust.
Where AI does the work
Uses available products
Seller onboarding
Business registrations, tax documents and bank letters are collected and examined, and the people behind a seller account are identity-checked.
Application direction
Refund evidence
Photos submitted with a damage or non-delivery claim are checked for AI generation and localized editing before a refund is approved.
Uses available products
Listing and review moderation
Detector24 models flag AI-generated product images and moderate images and text against the categories a marketplace prohibits.
Uses available products
Age assurance at checkout
Agemin estimates age from a face or an email address for age-restricted products, with an ID check when a higher level of certainty is required.
A damaged-item refund claim
An application of available models to a common retail process. It describes how the steps connect, not a customer deployment.
A customer reports a damaged item and uploads photos.
Each photo is checked for signs of AI generation and of localized edits, as separate signals.
DecisionNode combines the image signals with order details and returns a decision: refund, request more evidence, or review.
Clear claims are refunded at once. Uncertain ones go to an agent with the findings, so honest customers are not delayed.
In
- Photo of the base
- Photo of the shade
Out
- No signs of AI generationSeparate signal
- No localized editsSeparate signal
Questions from retail and marketplace teams
Can a refund be decided automatically?
In the workflow on this page, clear claims are refunded at once and uncertain ones go to an agent with the findings. Refund-evidence checks are an application of available models, not a described deployment.
Are AI-generated and edited photos treated the same?
No. Each photo is checked for signs of AI generation and for localized edits as separate signals, so a reviewer can see which one was found.
How does age assurance work at checkout?
Agemin estimates age from a face or an email address. When a product needs a higher level of certainty, an ID check can follow.
Other industries
All industries
Talk to Bynn about trust on your platform.
Bring the decision your teams make thousands of times a day. We will show you where AI can make it with better evidence.