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Artificial Intelligence


We help you to gRow your business using IoT AI strategy, Data management, data quality and data governance.

about our AI

We are looking forward to supporting you in your challenges around the use of AI. Learn AI with us and take the best out of it for your business. using machine learning methods, we create smart solutions that solve business problems and help people in everyday life. With our AI software development services we offer to companies and individuals support to understand and develop ML techniques and algorithms for predictions, anomaly detection, automation, and other applications. Make your device smarter by bringing AI to the embedded system level.

Our team can create machine learning models that 
will run on your IoT devices and ensure remote management and updates of models at the edge. We design algorithms that can learn from data and make predictions by building custom AI-based software solutions for healthcare, edge computing, IoT, image processing, automotive and other industries. As an embedded software development team, we design, code and test a wide variety of embedded systems applications ranging from a boot-loader to a GUI or even software for end users. Our embedded services include device driver development, legacy software migration, board support packages development for multiple operating systems (e.g. Android, Linux, RTOS) and software development for single-board computers.

Data Science

Data science is an important prerequisite for the development of AI systems. With the help of data pre-analyses and workshops, we identify the specific potential for improvement in your company.
Our data science team will help you make the most of the data you have. We perform data mining, data classification, and analysis to understand your data and the possibilities they provide to optimize your operations. Based on this data, our expert team can also support to create AI models for prediction, regression or classification tasks using the state-of-the-art techniques.

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AI Techniques

Our team has experience integrating numerous AI approaches to develop algorithms and full pipelines. These techniques include not only traditional approaches but also state-of-the-art techniques since our team keep working on researching new fields in order to ensure we can always provide the most suitable technique for each case.

Traditional techniques used in previous projects include:
New techniques the team researched and used for the most recent project include:
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AI Concept

Integrate data from different sources into a unified view that can be analysed and acted upon. It is necessary because organizations typically have data spread across multiple systems, applications, and databases, making it difficult to understand their operations. Our experts ensure that integration is consistent and usable, which leads to accuracy.

AI projects

Data  Gathering
is a key process in AI projects since the quality of the data highly impact the performance of the final pipeline. Because of this, our team can support to design the data gathering process for each case to optimize the consumed resources in this step. At the same time, our team can supervise this process to ensure the quality of collected data.

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Exploratory  Data  Analysis
Once data is available, data must be analysed in order to understand the characteristics of the data and how this data can be used for the specific application as well as what AI approach can be applied to study the data.

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Data  Processing

Raw data may need to be pre-processed to extract relevant features for the application. At the same time, sometimes the data needs to be converted into a different format such as converting punctual measurements into time series to study the temporal evolution of the signal or to extract temporal features. In other cases, raw data is complex and this would lead to a high complexity for the AI algorithm. In these cases, our team designs preprocessing techniques to convert the data into a reduced-complexity data that maintains the relevant information to be fed into an AI algorithm.

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Analytical  Modelling 

Using statistical techniques and machine learning algorithms to analyse data and identify patterns and trends in your data leads to efficiency and effectiveness. We include predictive modelling, clustering and segmentation. We identify the best course of action based on data analysis and make recommendations that help you achieve your objectives.

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Validation & Presentation

After an AI pipeline is designed and trained, it still needs to be validated to ensure it will work during the execution time after the project. For this, a subset of the data is reserved for the validation of the pipeline. This way, it is possible to confirm if the results are similar to what it was achieved in the experimental phases of if the pipeline needs to be fine-tuned.

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Hardware  integration

Integration of the designed AI pipeline into hardware is a crucial step in some projects. This step is not direct since some hardware devices may impose some constrains for the AI pipeline such as AI techniques implemented or available resources. Our team has experience with this step to ensure a correct definition of the requirements for the AI pipeline as well as the optimization for the hardware.

Some of the hardware platforms used in previous projects include embedded devices, FPGAs or TPU devices such as Google Coral TPU and Jetson Nano.

Applications / use case

Expand your horizons with data-driven intelligence. With our expertise in machine learning and multimodal data, we work closely with our customers to discover and evaluate all the possible use cases and conduct feasibility studies.

Some of the fields covered in previous projects include:


in one of the last IoT projects, our team worked on collecting data from IoT indoor sensors for indoor air quality prediction. For this, our team designed a pipeline where raw data is pre-processed to extract the relevant features that can be use for the application. After this, our team researched multiple AI algorithms to select the most suitable one for this task, also taking into consideration the constrains imposed by the hardware. In this project, the final pipeline was later converted and optimized to integrated in a PSoC device.

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Edge Computing

for some of our product development projects as well as research projects, there are limitations regarding the integration of the AI pipeline at the network edge. Therefore, our team took into consideration these limitations to defined hardware-oriented approaches such as Spiking Neural Network models or to optimize DNN models to be integrated into hardware devices such as embedded devices, FPGAs or neuromorphic devices such as Google Coral TPU or Jetson Nano.


Our team have worked in some healthcare applications. We have worked with biomedical signals to develop sleep studies to prevent diseases crises (PhD research). Moreover, a prototype of a non-invasive glucose monitoring device have been developed, optimizing the number of sensors needed to obtain the glucose level using AI techniques.

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Image Processing

For some classification projects, the collected data from sensors are not single values but images or data that can be study as images. An example of this is the study of radar data where our team converts the raw radar data into Range-Doppler images or Range-Azimuth images. Image or matrix data can be later studied using techniques such as Convolutional Neural Networks or deeper networks such as YOLO for classification or computer vision tasks.

Digital twins

Digital twins are an emerging topic where numerous technologies are being used to better represent the real world. Our team participated in European Funding projects to further research this topic regarding data-driven models for predictive maintenance of internal components. This can lead to an optimization regarding the timing for the maintenance of the systems to avoid possible maintenance delays or extra damage resulting from a late detection. In this project, our team developed multiple pipelines to detect when the soldering connections break, delamination levels in chips and possible internal problems in electrical motors.

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With the help of an inbuilt AI system, eesy People Counting Radar can discern between distinct crowds and eliminate superfluous counts, resulting in improved accuracy and scenario adaption for peak performance.


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Embedded software development

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Hardware Design

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Cloud Solution

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Artificial Intelligence


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