Architrave
Vishnu Surendran has a diverse work experience spanning several companies. Vishnu started their career as a Project Leader in the Kerala legislative assembly, working on a government webcasting project. In 2012, they joined Accenture as an SAP Functional Analyst and Quality Assurance professional. Vishnu remained with Accenture until 2015. From 2016 to 2017, Vishnu worked at CENIT, first as an intern on Artificial Neural Network and later as a Master Thesis Student focusing on C# development. In 2017, they moved to interfaceMA GmbH as a Full Stack Developer specializing in C#. Vishnu stayed with the company until 2020. Currently, Vishnu is employed at Architrave GmbH, where they began as a Software Engineer in 2020 and later transitioned into the role of Engineering Manager in September 2021.
Vishnu Surendran has a strong educational background in the field of computer science and software technology. Vishnu obtained a Master's degree in Computer Software Technology from Hochschule für Technik Stuttgart, where they studied from 2015 to 2017. Prior to that, they completed their Bachelor's Degree in Computer Engineering from the College of Engineering Attingal, graduating in 2012. Before attending college, Vishnu pursued a +2 degree in Computer Science from S M V HSS TVM, from 2006 to 2008.
In addition to their formal education, Vishnu has also obtained a certification in Duolingo German Fluency: Elementary (Estimated) in September 2016 from the institution Duolingo.
Architrave
Architrave is leading the digital transformation in Real Estate Asset Management by making document and data management faster and simpler. The company's platform and supplementary services enable the industry to make the step from physical documents to data and from data to smart decisions, allowing Asset Managers to actually manage assetsinstead of papers. With the application of machine learning algorithms to extract and process relevant information from documents, Architrave minimizes inefficient human work and solves the problem of non-standardized, poor data.