Case study · Consumer goods
AI makeover challenge
Repositioning a repair brand as a creativity enabler.
- Client
- Leading consumer brand
- Service
- Design and development
- Sector
- Consumer goods
- Status
- In production
At a glance
What the experience does
Upload, transform, share. Everything else is hidden behind those three.
Faces, nudity, profanity and other defined rules are checked before anything is restyled.
The transformation renders as a video rather than a still, because that is what gets posted.
The shareable video output is built to drive product affinity and reach, not just to delight one user.
The brief
Moving a brand from the cupboard to the craft table
Some products have a fixed place in a customer's head. A repair product is reached for when something breaks, which caps how often anyone thinks about it. The brief was repositioning: the same product, understood as something you make things with rather than something you fix things with.
Telling people that does not work. Showing them their own object, transformed, does. Which turns a brand problem into a generative AI problem with a very awkward constraint attached: you are inviting the public to upload photographs, and then publishing what comes back under a brand's name.
An open upload field and a brand's reputation in the same product. The screening layer is not a feature, it is the permission to ship.
How it works
Three steps for the user, four for the system
Upload
The user photographs any everyday object. No setup, no constraints on what the object is.
Screen
Vision models check the upload against defined rules covering faces, nudity, profanity and more, before anything else happens.
Transform
The object is restyled into a finished DIY project, generated from the user's actual photo rather than picked from presets.
Share
The transformation renders as a video built for posting, which is what turns one user into an audience.
The build
The screen comes before the model
The ordering is the whole design. Every upload is screened by vision models before restyling — faces, nudity, profanity and other defined rules. Nothing reaches the generative step until it has passed, which means the brand is never in the position of having generated something from content it should have rejected.
The output format is the second deliberate choice. A still image is a result; a video is a post. Rendering the transformation as a shareable video is what makes the mechanic drive product affinity and virality rather than just delighting one person.
Wow Labz designed and developed the experience. The client is not named at their request and the project is in production.
Stack
What it was built with
Questions
The makeover challenge, answered
What does the experience do?
A user photographs any everyday object and the AI transforms it into a finished DIY project, rendered as a shareable video. The point is repositioning a repair product as something people create with.
Who was the client?
A leading consumer brand. The project is in production, so the brand and category are published without naming them until the client chooses to announce it.
How is an open upload field kept safe?
Every upload passes through vision models before anything is generated from it, checked against defined rules covering faces, nudity, profanity and more. The screening step runs first, so the generative step never sees content that should have been rejected.
Why a video rather than an image?
Because the output is meant to be posted. A video is the format that carries a transformation to an audience, which is what turns a single user's result into product affinity and reach.
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