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About Loop54

Our leading proprietary AI helps eCommerce leaders overcome the barriers of innovation – swiftly implementing powerful personalisation to create truly relevant customer experiences.

+97%

revenue

+42%

value per search

+30%

conversion rate

Loop54-founders-about-us

Today, Loop54’s powerful eCommerce personalisation technology platform is trusted by leading eCommerce businesses.

We provide one-to-one personalisation on both mobile and desktop, delivering speed and relevance as a complete product search platform.

The mission

What do you get when you combine a Mathematician and a Software Engineer? A new approach.

While others engaged problems of on-site search as solely Engineers (focusing on efficient database indexing and query alteration), Mike and Joel took a mathematical approach.

Combining their expertise, Loop54 modelled relations between products using different techniques ranging from Graph Theory to complex Neural Networks.

What Loop54 can do:

  • Supports exploratory process of mathematical modelling & rapid innovation
  • Creates isolated code that allows plug & play functionality, without affecting anything else
  • Lets Mathematicians work and optimise independently of Backend Dev
  • Enterprise-level personalisation with state-of-the-art machine learning models
  • Developer-friendly

How it works

Founders

Loop54-Robin-Mellstrand

Robin Mellstrand

Co-founder and CEO

Loop54-Mike-Odin

Mike Odin

Co-founder and Head of R&D

Loop54-Joel-Kall

Joel Kall

Co-founder and Sr. Developer

The evolution of our models and algorithm

online-store-machine-learning

2012

Topic Model

Topic Modelling

Determined underlying structure of the catalogue by reducing the complexity of a products meta-data to abstract 'topics'. Created the initial ability to find Related Results.

batman-for-ebusiness

2013

Batman

Behaviour Augmented Topic Model and Association Network (Topic Modelling)

Added graph theoritical elements to reinforce the topic model. Improved robustness of 'topics'. Added behaviour-based learning and initial personalisation.

bean-AI-for-eCommerce

2015

Bean

Behaviour Enhanced Association Network (Graph Theory)

Created a completely new way of representing the catalogue structure inspired by social network analysis and graph theory. Improved reliability and ease of configuration. Reduced model training time by 90%.

golem-machine-learning-ebusiness

2016

Golem

Generalized Organisation by Layered Expanding Maps (Neural Networks)

Created a multi-layered abstraction of the product catalogues structure - analogous to the structure of a well mechandised physical store. Increased conversion rate by 8% compared to BEAN.

golem-phat-ebusiness

2018

Phat Golem

Generalized Organisation by Layered Expanding Maps (Neural Networks)

An evolution of GOLEM where we used swarm intelligence algorithms in model training. Significantly reduced training time and increased robustness of generated model.

Gas-AI-for-eCommerce

2020

Gas Golem

Generalized Organisation by Layered Expanding Maps (Neural Networks)

Greatly increases model adaptability to changing product sets and handles streams of mixed-data. Attempts to extract humanly readable insights from the abstract model.

Discover more about Loop54

See how e-tailers transform customer experience using our eCommerce personalisation technology.

View Case Studies

About-us-loop54-technology