What we do & How we help
Graph Algorithms or Graph Analytics are analytic tools used to determine strength and direction of relationships between objects in a graph. The focus of graph analytics is on pairwise relationship between two objects at a time and structural characteristics of the graph as a whole. Typical terms and concepts in this context are centrality measures, clustering, reciprocity, spanning trees and more. Graph analytics can be part of graph visualization provided the data is small but one typically implements things in middleware in order to pre-process big-data using Spark, Databricks or similar stacks. There are many ready-to-go solutions on the market like Linkurious Enterprise and Cambridge Semantics but some problems (and domains like anti-terrorism and intelligence) require custom development and techniques.
We help companies approach challenging problems and have a broad experience in many industries. We have developed solutions involving natural language processing (bots e.g.), PhD-level techniques to optimize digital marketing, big-data graph mining solutions towards fraud detection, custom graph machine learning models and more. Technology is for us a tool and we are in this sense technology-agnostic, whether your preferred stack is .Net, Python, Java or Rust, we focus on finding a business solution.
As an independent consulting company we help you find a path through the technology jungle. While the well-known consulting firms will advice you using off-the-shelf enterprise packages, we provide an objective view and long-term solution within the scope and budget of your business.
We help defining and developing algorithms to detect fraud rings and anomalous behavior. Combining time series analysis and pattern detection in relational and/or geospatial data, we help you turning data into smart decisions.
Graphs are key when looking at organized crime and cross-usage of assets.
How to see the narrative behind criminal cases? What is the story unfolding, what are the actors, the arena, the modus operandi?
We help combining data from different sources, visualizing and analyzing large graph repositories or help create them.
Using advanced machine learning, we help create inference engines and predict crime.
We help with the big picture and the small details, visualizing in various ways how large amounts of data create stories, how evidence creates proofs, how time and space joined with relational data create facts you can use in legal cases.
Graphs help complement the relational and tabular data, they allow centrality analysis and path finding, they allow to ‘see’ aspects hidden in the haystack.
Epidemics can be seen as the transfer of information across networks and connected domains. The techniques to analyze epidemics apply well beyond the biological systems; the spreading of fake news, computer viruses, the adoption of fashions, cascading failures in electrical power grids, ecology of multiple species and so on.
We help companies analyze and define patterns, develop the appropriate tools and software and ‘see’ how the generic patterns evolve in their concrete business context.
Graph schema’s and ontologies are not very well known in many industries. Many NoSQL and graph solution even ignore it altogether. If you need a long-term knowledge management solution we help with creating ontologies and guide you through the many hurdles towards a knowledge base you can effectively use.
Whether you prefer triples or custom solutions like GRAKN.AI, TigerGraph or similar, we are here to help.
How to turn natural language into re-usable knowledge, how to store it and how to turn it back into language? How to organize domain-specific data and query it all?
Knowledge graphs and ontologies go together and there is a wide variety of options as can be seen from our graph database overview. We guide companies through the jungle of storage systems and software stacks.
Graphs are the secret ingredient when dealing with digital marketing optimization. Finding the hotpaths towards conversion, the transitions between touchpoints or modeling a Markov chain? We can help.
We helped very large corps applying graph analytic models to marketing challenges and seen big data in all its glory deliver its promise.
Social Network Analysis
Social network data has become the go-to data when augmenting in-house marketing data. We help companies with the creation of heterogenous networks and how to apply graph analytics to them.
Natural Language Understanding
Smart decision-making and (artificial) intelligence is never far away from NLP. We help companies applying deep learning to text and speech, how to convert language to graph structures (semantic labeling, question asking…) and how language in general can be used to augment existing data.