Malvern-based D-RisQ, has won an EU platform programme that will give visibility of their technologies across the EU. The HUBCAP programme specifically targets SMEs with high capability model based design tools and provides an environment for other innovative SMEs to try before they buy.
HUBCAP also provides the opportunity to D-RisQ to compete for further funding with innovative SMEs who want to run cyber-physical projects that demonstrate both D-RisQ tools and the innovation, for example, in the safety assurance of autonomous systems.
D-RisQ technologies are already helping innovative SMEs in the UK develop highly assured new capabilities in aerospace and autonomous systems and HUBCAP significantly expands the opportunity space. Currently, the highly robust technologies are bringing cost savings of up to 80% in software development in autonomous systems for nuclear decommissioning and offshore underwater infrastructure inspection as well as in aerospace applications.
Nick Tudor, Chief Executive Officer at D-RisQ, says:
“This is excellent news for D-RisQ as it raises our profile across the EU and enables innovative SMEs from the UK and EU to learn how to build new cyber-physical systems safely and assuredly while saving them money. This will give the opportunity to get to market much faster and at lower cost bringing advantages to communities across many sectors.”
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NOTES TO EDITORS
CEO, D-RisQ Ltd.
Email: [email protected]
Tel: 01684 252452
About D-RisQ Ltd
Founded in 2012, D-RisQ specialises in the development of automated software tools that improves system safety across a host of industries, including aerospace, automotive and security. Located in the Malvern Hills Science Park, the tools will revolutionise the way that the world’s electronic systems with software are developed. The technology provides absolute proof of behaviours without much of the need for manual review and test, so drastically reducing development time and cost. This is an enabling technology for highly complex interactive systems with autonomy and machine learning, which is currently struggling to assure
system behaviours through traditional test methods.