Help customers see more,
and choose better.
AI in your store
From the first question to the right product.
Virtual try-on shows a product in the context of a customer’s own photo. An AI recommendation system helps them find relevant options, while shopping prompts provide useful information at the right moment.
We bring together the catalogue, size guidance, current promotions and information about payments, delivery and returns in an experience shaped around the brand.
Virtual try-on
A product from your catalogue.
A preview on your own photo.
Customers open try-on from a product page, confirm the item and choose a photo from their library or camera. AI creates a visualisation of the selected garment.
The preview helps explore shape and colour. It does not measure the body or guarantee size, fit or fabric drape. The size chart remains a separate reference.
LE POSSÉ
Help at the moment
it is needed.
On the product page, the system offers help with sizing or a virtual try-on of the selected item. Customers can explore the product and open the relevant feature without searching elsewhere in the store.
Prompts highlight the current promotion and can show available deferred payment options after an item is added to the cart. The recommendation system suggests other products and helps complete the selection.
Delivery, returns and purchase information remain close at hand. Messages reflect the store’s current offer and terms, with an interface that follows the LE POSSÉ identity.
AI recommendation system
Customers describe a need.
The store presents options.
Customers may not know a product name, but they know the occasion, style or topic they have in mind. Short questions and preference choices narrow the catalogue to relevant suggestions.
Recommendations can include an image, description, price and product link. We connect them with shopping information and the agreed cart features.
PAT ARROW
Fragrance, interiors, jewellery.
A choice guided by intent.
The adviser starts with a goal: fragrance, diffuser, jewellery, styling or a gift. For perfume, customers can describe the desired character or name a fragrance they already enjoy.
Suggestions lead to products and help refine the choice, such as a lighter composition or a sample set. Guidance is advisory because fragrance perception is personal.
Natalia Kutyła
Knowledge and products.
One recommendation system.
The system helps customers choose educational materials, training and consultation options. They can start with a topic such as laboratory results, gut health, skin or supplementation.
The chosen topic leads to related catalogue materials. The panel supports educational product selection; it does not diagnose or replace medical consultation.
From idea to implementation
We organise products, variants and recommendation data. We define selection scenarios and when to show guidance about sizing, promotions, payments, delivery and returns.
We adapt typography, colours, animation and tone of voice. We design desktop and mobile interactions with accessibility in mind.
We connect the agreed parts of your store and test choices, links, errors, mobile devices and cases without a suitable recommendation.
Before launch, we agree how data is processed, what customers are told and what is measured. Actual usage informs further improvements.
Designed for your catalogue and your customers.
We start with a specific use case. Virtual try-on, recommendations and informational prompts can be introduced in stages to suit the store.
Questions and answers
What should you know before implementation?
Yes. Size guidance can use product information and size charts. Virtual try-on is a separate feature: it visualises clothing on a photo, but does not measure the body or guarantee fit.
Try-on is for clothing, while recommendations can support many catalogues, including perfume, jewellery and educational materials.
Yes, when included in the integration scope. Product links and selected shopping actions are possible. We verify the scope in the target store.
The catalogue, conversation scenarios, integrations, interface design and usage scale. We agree the scope and running costs before implementation.
