Tech Retail New Mobile
Necessity is the Mother of Invention
 
Over the years, we tackled many complex problems and have used different tools and technologies that are part of the standard arsenal: linear and logistic regression, neural networks, decision trees, random forests…
 
But many times, we found that we were better served by using graph analytics. So, we made significant investments and developed LynxKite, our own graph analytics tool, which we use for most AI projects.
 
It allows us to cut through complex problem faster and deliver granular answers at scale like no other technology on the market.
 
Call it our secret sauce.
Pioneering Graph AI
 
We continue to put R&D efforts into graph analytics and have developed ground-breaking applications where we use AI techniques such as Deep Learning and Steiner Trees on graphs.
 
We call this “Graph AI”. This keeps us on the cutting edge of AI technologies and ensure we can continue to deliver the best solutions to our customers.
  • What is Graph Analytics?

     

    Graph analytics is the process of analyzing data sets as graphs (or networks in plain language), where data points are expressed as nodes and the relationships between these points are represented as edges.

    By using graphs analytics, we can uncover for example the impact of discounting a product on the sell-through of related products. Retailers can use these insights to optimise commercial decisions about discounting, bundling, cross-selling and more.

  • What is Graph AI?

     

    Graph AI is the science of using Machine Learning on graphs to focus on the relationships between variables to achieve deeper insights.

    By using specific algorithms like clustering, partitioning, PageRank and shortest path, some problems become easier to solve.

    These include problems where centrality, connectivity, and path analysis play a key role in the analysis.

  • What is Graph Analytics?
  • What is Graph AI?

What is Graph Analytics?

 

Graph analytics is the process of analyzing data sets as graphs (or networks in plain language), where data points are expressed as nodes and the relationships between these points are represented as edges.

By using graphs analytics, we can uncover for example the impact of discounting a product on the sell-through of related products. Retailers can use these insights to optimise commercial decisions about discounting, bundling, cross-selling and more.

What is Graph AI?

 

Graph AI is the science of using Machine Learning on graphs to focus on the relationships between variables to achieve deeper insights.

By using specific algorithms like clustering, partitioning, PageRank and shortest path, some problems become easier to solve.

These include problems where centrality, connectivity, and path analysis play a key role in the analysis.

Our Approach 

 

Learn more about our adequate mix of disciplines such as business consulting, application development, data science, data engineering, and of course, change management.

Learn More About Our Approach