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What is a Digital Twin & practical steps to build.
We have built Digital Twins to solve complex data problems on a range of projects. Our clients include:
Testing of complex systems: We developed testing tools and synthetic data. This solved a decade long problem to create valid data sets for the XBRL reporting standard in Financial Services. Our solution exercised over 8000 validation rules.
User friendly front ends for highly complex domains: We ingested and made congruent 1000’s of pages of their regulatory handbook. We also integrated their data dictionary and regulatory reports. We used ModelD plugins to give end users flexible, simple and powerful ways to search and analyse the regulations.
Root Cause Analysis: Our client had previously been unable to answer root cause queries about their network. We built a holistic view of a global network using data from diverse network systems. We then built a DSL which provided answers to these complex queries.
Regulatory Reporting burdens: We teamed with a regulatory reporting provider to win an industry hackathon. We demonstrated how you could transform Regulatory Reporting through the use of Domain Specific Languages to make a single congruent model connecting the Regulators Handbook with Bank’s databases via regulatory reporting engines. This spurned an industry initiative, Digital Regulatory Reporting