• Graph Data Science Consulting

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

Services & Expertise

Company

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.

Large scale graph layout (with edge bundling), network visualizations and #dataviz on GPU thanks to @datashader. Perfectly complementing @RAPIDSai. https://t.co/mijYt1Lc1B https://t.co/hzf9moEIwU
AuraDS, the @neo4j data science stack in the cloud is in preview. https://t.co/9BWY7t7kJh #GraphDatabase https://t.co/d79x4UlhYp
A semantic graph database on top of #Blockchain, yes you can. Fluree is a crypto-secure immutable graph database with cloud-native architecture. Open source 😳https://t.co/BGj2Ha9VPj @FlureePBC #GraphDatabase https://t.co/Ulv956lFk4
Frictionless, innovative graph applications, graph visualization and graph analytics with Memgraph. https://t.co/bj5KooRPme @memgraphdb #GraphAnalytics #GraphDB https://t.co/WjqXAKgYJu
Not the usual intro to graph representation learning by Michael Bronstein @mmbronstein, Love the big picture, the graphics and low-tech story-telling. https://t.co/ppVJQp1XUr #geometricdeeplearning https://t.co/AoNxSgeF6f
Latest @neo4j graph library introduces "Link Prediction Pipelines". Democratizes graph machine learning but for heterogenous data and other subtleties you'll still need @stellargraph_io or #PytorchGeometric. https://t.co/tbwOf253M3 #graphneuralnetworks #knowledgegraphs https://t.co/qcoEqzXmCV
Deep Learning’s Diminishing Returns: how the financial cost increases exponentially when trying to improve a model just a little bit. Same as in elementary particle physics. https://t.co/TTJZMGrYcL #machinelearning https://t.co/kzltWiXEfi

Partnerships