From 2012 CeADAR’s research team has been helping companies deploy AI and analytics solutions across a wide range of industries, making these companies highly competitive in an increasingly fierce global competition landscape.

The CeADAR team is composed of more than 50 expert Data Scientists with strong industrial experience. The CeADAR centre’s active role in the European AI ecosystem helped the centre remain ahead of the curve in terms of AI expertise.

Our team of world-class AI experts can help you define the next steps you can take now to stay ahead of your competitors tomorrow. We can offer bespoke solutions to our clients in all sorts of industries by applying cutting edge AI technologies. We adapt to our client needs and can collaborate on short term projects or deliver a large scale AI solution. A sample of the sectors that CeADAR works in are listed below.

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The analysis of data generated by IoT devices such as temperature, moisture, soil fertility, rainfall, etc. can provide valuable insights- known as precision agriculture – that can help the farmers to increase productivity, reduce cost, restrict the use of chemical fertilizers and water.

IoT for e-Health

Aggregate the data generated by a combination of home monitoring sensors, wearables and subject’s historical information and apply the latest techniques in machine learning and recommender systems can help detect and predict anomalies in the behaviour of patients and match them with the right practitioner.

Smart Cities

Cities are generating an increasing amount of data such as traffic, industrial activity and  air quality data. The aggregation and the analysis of this data enable the development of systems that can predict the consequences of human activities on the environment.

Financial Services

ipad Image by dawnfu from Pixabay

NLP-based software can help search for specific information from internal documents. The algorithms can be trained to recognise attributes in documents that are marked as being relevant for extraction, summarisation, or abstraction. Integrating such systems could reduce the cost and time associated with information search inside the enterprise.

Renewable Energy

Windfarm Image by Barbara McLullich from Pixabay

Applying deep learning and big data techniques to model the way wind turbines operate and generate electricity based on weather data can help renewable energy companies predict electricity production and plan ahead of time.


Machine learning has recently found many applications in remote sensing. These applications range from crop classification to detection of disease in crops.


Writing Image by Free-Photos from Pixabay

Computer vision based model can enhance the ability to detect, measure, and respond to individual students’ levels of engagement and performance.