Customer Case Studies
Our customers build pioneering new technologies. The combination of our data visualization innovation and their specific industry expertise has changed how their users view their connected data, unlocking new and exciting dimensions of insight.
So, why do they choose KeyLines? Our case studies will explain more.
How our partnership with Microsoft Services UK is helping police forces improve their response to incidents.
How Logtrust empowers analysts to harness the connections in their big data, with the help of the KeyLines toolkit.
In 2015, EclecticIQ used the KeyLines network visualization toolkit to build an interactive graph visualization that sits at the heart of their platform.
How our partnership with IntelligentTag will help organizations in the regulated industries to harness the power of Neo4j.
This case study explains how iMapData have used the power of the KeyLines network visualization toolkit to help customers understand and mitigate risk.
Learn how Cifas, the UK’s anti-fraud authority, has deployed KeyLines to allow its 300 members to visualize the UK’s national fraud database.
Learn how Siren Solutions used KeyLines to build a powerful graph exploration and visualization component for Elasticsearch indices.
How KeyLines is being used to help CyberFlow customers achieve 360-degree cyber situational awareness, and mee the scale and complexity challenges posed by the ‘Internet of Things’.
Cambridge Semantics, the leading provider of smart data solutions driven by Semantic Web technology, uses KeyLines in its Anzo Smart Data Platform.
In 2013, we helped one of the world’s biggest insurance companies enhance their SAS fraud discovery and analysis solution with a custom-built KeyLines GUI.
How one global market leader is using KeyLines to give an intuitive and accessible way to visualize and understand IT networks, in real-time.
What do our customers say about us?
Our customers are delighted with this new approach to data exploration. We have transformed real-time discovery on massive data sets from a laborious process of reviewing lists to rapid-fire graph analysis.