• Graph Thinking & Consulting

    We help companies turn multi-dimensional data
    into actionable insights through graph analytics and
    graph visualizations.

Services & Expertise


Orbifold B.V.
Leuven, Belgium.

Phone: +32-498-103288
Email: info@orbifold.net
Twitter: @theorbifold

Orbifold Consulting specializes in articulating graphs as a tool to extract business insights from data. Operating as an independent consulting company for more than 20 years, we combine business expertise and scientific know-how in bespoke software solutions. We deliver unique and innovative solutions using state-of-the-art tools and technologies.

We count amongst our customers world-renowned enterprises across all industries. Graph techniques can be applied to almost any business domains but are particularly well suited to fraud analysis, marketing optimization, operational intelligence, anti-terrorism, forensics and any form of large-scale knowledge management. Our expertise in these domains is broad and deep; from PhD-level scientific research to sophisticated JavaScript front-end development, from advanced graph machine learning techniques to cloud devops, from management consulting to startup boosting.

Being vendor neutral enables us to put together the best technology for every project, we’re innovation partners from ideation to implementation.

Beyond message passing, a physics-inspired paradigm for graph neural networks. https://t.co/viPTHXaPo4 #GraphMachineLearning https://t.co/BKqGJom5pz
Leo Meyerovich from @Graphistry on how and why the best companies are adopting Graph Visual Analytics, Graph AI, and Graph Neural Networks. https://t.co/GQNiKH9EJ4
#GraphAnalytics #GraphMachineLearning https://t.co/g3YYh8UcT2
TigerGraph Machine Learning Workbench is in preview and comes with a custom Jupyter Lab, plus lots of goodies to hook up your favorite graph ML framework. Exciting times ahead. @TigerGraphDB https://t.co/68Xe35GPOt #GraphMachineLearning https://t.co/2Lz0w5NuTm
My article on drug repurposing is now also published on the @TigerGraphDB blog. Thanks to the great Tigers and @CayleyWetzig in particular. https://t.co/uMV1aTvUrr
#healthcare #GraphMachineLearning #KnowledgeGraph https://t.co/inQbftYJGX
If you want to explore the knowledge graph visually in my latest article there is, besides yFiles (https://t.co/QR6wmzOJNS), also @Graphistry (https://t.co/IdqMN7NiNO) which manages to handle very large graphs in the blink of an eye.
https://t.co/RzudOW7DKu https://t.co/CoDtw7RkAQ
Drug Repurposing Using TigerGraph & Graph Machine Learning. https://t.co/MdriWAJzJr #GraphAnalytics #KnowledgeGraphs
https://t.co/QuE2AoYAyk https://t.co/gGNlCQselk
All paths lead to one. A Collatz graph visualization with 1.5 million nodes doesn't prove anything but certainly supports the conjecture. Layout based on custom Wolfram code. #GraphAnalytics
https://t.co/TBqkHzkp6U https://t.co/2WriphA2R4