E-COMMERCE

MQ Fit digitally recreates the real life usage of any product in just seconds.

MQ Fit is a 3D avatar based fitting software, which helps to choose the right product. This virtual platform digitally recreates the real life usage of any product by creating a 3D avatar out of just two pictures of a customer and letting it virtually test the product in just seconds. It solves sizing problems and offers highly personalized shopping experiences and compelling, reliable recommendations.

CUSTOMIZED TO YOUR NEEDS

We integrate brand identity by default, with additional customization options available as needed to provide a seamless, on-brand experience for your customers.

OMNICHANNEL

An ideal omnichannel solution optimized to solve sizing issues for customers regardless the sales channel.

SALES UPLIFT

Product suggestions actively boost sales opportunities on your website based on scientific data and in-depth professional expertise.

RELIABLE RESULTS

We help companies process big data, enable faster decision-making, and solve complex challenges through our machine learning services.

The guided process starts with obtaining personal data, including height, weight, and age.

Next, the user uploads two full-body photos or takes them on the spot using a smartphone or tablet.

With AI-driven algorithms and biomechanical models, the customer’s individual 3D avatar is created and used to simulate an interaction with the provider’s previously measured product.

The ideal product is recommended within seconds.

WIN-WIN SITUATION

USE CASE

Digital recommendation engine for mattresses

We have developed the digital recommendation engine for mattresses – a consumer-centered technology that helps choose the right mattress online within seconds, providing an excellent user experience. Provide your customers with the ideal product for a healthy and comfortable sleep.

Scroll to Top

DOWNLOAD OUR NEW ECC STUDY

Our international study surveyed  1500+ consumers‘ expectation towards the online-shopping of advice-intensive products.
Download the findings on how manufacturers and retailers can optimize their product recommendations for a more satisfied customers and less product returns!