Makersite
Neil Dsouza has a diverse work experience spanning various industries. Neil started their career as a Software Engineer at Meerestechnik Bremen GmbH in 2005. In the same year, they also worked as a Software Engineer at International University Bremen. In 2007, they joined LHS Telekommunikation as a Consultant. In 2011, Neil moved to thinkstep, where they held multiple roles including Senior Consultant from January 2011 to February 2013, VP Corporate Sustainability from March 2013 onwards, and VP Product Development from March 2014 to November 2016. Currently, Neil is the CEO of Makersite since February 2016.
Neil Dsouza obtained a Bachelor of Science degree in Geosciences and Astrophysics from Jacobs University Bremen. Neil pursued further education at the University of Bremen, where they completed a Master's degree in Environmental Physics between 2005 and 2007. Specific details about their field of study during both degrees were not provided.
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Makersite
Makersite is a cloud-based product data management tool that helps companies manage product sustainability, cost, and compliance. Product life cycle management involves making design decisions based on multiple criteria including cost, compliance, sustainability, and risk. Unfortunately, the data and expertise required to make these decisions aresiloed. This protracts the process of innovation and increases its complexity. Today, the market solves this problem with vertical applications like PDM, ERP, CAD, EHS, SCM, etc. These mostly remain siloed due to the enormous costs of integration and keeping data synchronized. Therefore, analyses typically require exporting data to aggregation tools e.g. BI or excel before being used for analyses in specialist decision support applications. Results are typically delayed, some taking as much as 9 months, and therefore provide little support during the design process. Makersite provides results instantly and simultaneously across key product criteria.Makersite combines external and internal data sources to create a digital twin of a product in design. Artificial intelligence and its graph-based data model allow for ingesting, representing, and connecting heterogeneous data easily. Its native applications use algorithms to support analysis and decision-making based on multiple criteria simultaneously including should-costs, regulatory compliance, life cycle impacts (LCA), supply chain risk, etc. The API-first architecture allows for easy integrations into existing IT infrastructures thereby supporting systems and processes with richer, fresher, and more timely product data.This allows engineers to understand and improve their designs from the perspective of their regulatory compliance, environmental impact, supply risk, and cost of production, simultaneously. Companies can get results up to 40x faster than traditional methods while making their products better.