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ShimmerCat for Business - Development of faster and more intelligent e-commerce websites with machine learning

Reference number
Funding from Vinnova SEK 300 000
Project duration October 2017 - September 2018
Status Completed
Venture Innovative Startups

Purpose and goal

The aim of the project was to develop a product to create faster and more intelligent e-commerce websites with the use of machine learning. The foundation of the project is the proprietary HTTP/2 software ShimmerCat, which uses data collection and analysis through AI and machine learning to reduce loading times for e-commerce websites. The goal of the project was to develop features related to customer user experience, and to develop technical features to further improve loading times with the use of AI and machine learning.

Expected results and effects

The project results have been satisfactory, and they clearly show the benefits of having a data-driven e-commerce business. Thanks to the results, e-commerce websites can now easily reduce loading time by simply adding ShimmerCat to its existing IT infrastructure. ShimmerCat then manages the communication with website visitors which enables it to collect data such as network conditions, device, and geographical location. This data is then optimized with AI and machine learning to reduce loading times, and the expected effect is a website that loads 40-60% faster.

Planned approach and implementation

With the new HTTP/2 protocol, launched in late 2015, the market for creating fast websites completely changed. The arrangement for this project has been to continue the innovative work initiated by scientists and researchers who used the new technology to create a groundbreaking software for creating faster loading times for websites. Today´s market with services for creating faster web pages is adapted to old 90´s technology, and with the implementation of this project, we want to open up for a entirely new and improved solution for a well-established market.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 8 January 2019

Reference number 2017-03989

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