Trend Tailor-personalized fashion recommendations.

Example

A user looking for a business-casual outfit for a conference might describe their preference for a navy blazer, slim-fit trousers, and loafers. Trend Tailor would break down this request into individual queries, searching for each item with specifications for gender, style, fit, and color.

Scenario

In a scenario where a user wants to refresh their wardrobe with versatile pieces, Trend Tailor could recommend a series of staple items like a white button-down shirt, dark wash jeans, and neutral-toned sneakers. Each recommendation would be carefully selected based on the user’s input, ensuring a cohesive and stylish result.

Example

When a user uploads a picture of a celebrity outfit they admire, Trend Tailor uses image recognition to dissect the outfit into individual components, such as a leather jacket, graphic tee, and ripped jeans. It then queries the ASOS database for similar items, considering factors like gender, fit, and color.

Scenario

Imagine a user is preparing for a beach vacation and requests an outfit. Trend Tailor would sequentially recommend items like a pair of swim shorts, a light linen shirt, and flip-flops, all matched to the user’s style and preferences through structured API queries.

Example

If a user isn’t fully satisfied with the initial recommendations, they can provide feedback, prompting Trend Tailor to refine the search. For instance, if the user finds a recommended dress too formal, Trend Tailor can adjust the search criteria to find a more casual alternative.

Scenario

A user might request an evening gown for a gala but expresses a preference for a specific color or silhouette. Trend Tailor would offer several options, and based on the user’s feedback, narrow down the choices to a gown that perfectly matches their vision.