ITEA is the Eureka Cluster on software innovation
ITEA is the Eureka Cluster on software innovation
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Bewell Teknololji San.Tic.A.Ş.

Bewell Technology; is an information and communication technology company established in Eskişehir in 2017 to develop new projects, conduct R & D activities and provide appropriate software solutions. It develops software that organizes business processes for companies of different sectors and sizes. We have an innovative, open-minded and enthusiastic staff who follow the latest technology and come from different disciplines aim to provide optimum solutions to our customers. Our company is active in the fields of Digitalization, Factory of the Future, Smart Manufacturing. We have expertise in AI, IoT Technology, Data Mining, Data Monitoring & Management and software development. We use artificial intelligence techniques such as Machine Learning, Deep Learning, and Natural Language Processing to develop intelligent systems that minimize human intervention. Also we collect data from various sensors by using the Internet of Things Technology that came into our lives with Industry 4.0, working on the collected big data and creating the most optimal model to develop a solution to the problem. We use Text Mining, Web Mining and Data Analytics methods to analyze big data and make estimation on the developed model. Since the companies do not have sufficient capacity to store the data increasing day by day, we are able to develop and manage open source software in real time. We develop software that runs on platforms such as Windows, Linux, Android and as web interface according to the needs of our customers.

Type
Small and Medium sized Enterprise
Country
Alt Türkiye
Website
http://bewelltech.com.tr

Documents

Project participation

ITEA Call 2023
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AdOff

Adaptive Office

ITEA Call 2023
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PHRESH

Patient Health Response in Emergent and Secure Habitats for Connected Healthcare

ITEA Call 2021
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DAIsy

Developing AI ecosystems improving diagnosis and care of mental diseases

ITEA Call 2021
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TiDiT

Timeline-Driven Digital Twin