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We help you to

Grow your business using IoT AI strategy

, Data management, data quality and data governance.
our AI service

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 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.

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 projects.
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 you in the creation of AI models for prediction, regression, or classification tasks using 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 traditional approaches as well as state-of-the-art techniques, and our team is constantly exploring new areas, ensuring that we are always able to offer the best technology 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 multiple sources into a unified view that you can analyze and act on. This is necessary because an organization’s data is typically spread across multiple systems, applications, and databases, making it difficult to understand its operation. Our experts ensure that the integration is consistent and easy to use, resulting in accuracy.
AI projects

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

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Exploratory  Data  Analysis
Once data is available, data must be analysed in order to understand its characteristics, how it can be used for a specific application, and what AI approaches can be used to examine it.
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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, which would lead to a high level of complexity for the AI algorithm. In these cases, our team designs pre-processing techniques to convert the data into 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:

One of the fundamental purposes of AI applications is to act as a “prediction engine” and provide insights to drive actions that improve operations and performance. Our team worked on collecting data from different sensors for indoor air quality prediction. To achieve this objective, our team designed a pipeline that pre-processes the raw data to extract relevant features that can be used for this application. Next, our team considered multiple AI algorithms and selected the best one considering the constraints of hardware limitations, as AI algorithms run on embedded devices. In this project, the final pipeline was later translated and optimized for integration into embedded devices.

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Integrating AI pipelines is often a challenge for network edge solutions since it has certain limitations. Therefore, our team took these limitations into account and defined a hardware-oriented approach, such as the spiking neural network model. Another approach was to optimize his DNN model for integration into hardware devices such as embedded devices, FPGAs, and neuromorphic devices (Google Coral TPU or Jetson Nano).

AI can be used in a variety of healthcare applications. Sleep studies to prevent disease crises are one of the use cases that our team addresses working with biomedical signals. Additionally, a prototype non-invasive blood glucose monitoring device was developed that uses AI technology to optimize the number of sensors needed to detect blood sugar levels.

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Image Processing

In some classification projects, the collected data from sensors are not single value but images or data that can be studied 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. The image or matrix data was later studied using techniques such as Convolutional Neural Networks or deeper networks such as YOLO for classification or computer vision tasks.

Digital twins

Our team further investigated this topic with respect to data-driven models for predictive maintenance of internal components. This may optimize the timing of system maintenance and avoid additional damage due to delayed maintenance or detection delays. In one of our projects, our team developed several pipelines to detect when a solder connection breaks, how much a chip delaminates, and whether an electric motor has internal problems.

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Tracking and counting people can be done using innovative technological approaches, such as the use of radar technology. This innovative approach avoids data protection issues. To understand and process information from radar, AI systems must be built to process raw radar data. Using an integrated AI system, people counting radar can distinguish between different crowds and eliminate redundant counts, improving accuracy and enabling scenario adaptation for peak performance.


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