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Applied AI

Data – your raw material for smart innovations

In all modern digital infrastructures (and in most older ones), large amounts of data are continuously produced, but they are fragmented and disparate. Essential information contained in this data cannot be used optimally due to the distribution and disparity. comlet helps you to lift your treasure trove of data and make it usable.

Barriers to AI implementation

As multi-layered as the application areas of AI methods are, as complex is the implementation in practice. Data strategies, which must be the basis of any AI strategy, are often not mature since heterogeneous environments, in many cases packed with legacy devices, provide information in an inconsistent manner. Subsequently, expectations of AI are extremely high or concerns are strong, with a lack of realistic assessments. All stakeholders need to be involved at eye level in the development of an AI strategy to overcome common problems:

Lack of a holistic data strategy

Heterogeneous network and device environment

Lack of AI expertise

By the way: Artificial intelligence (AI) refers to the application of human-like decision-making and thought processes, while machine learning (ML) describes methods that can independently derive models from data and thus learn from information. ML is therefore de facto a subset of AI.

The road to AI

From data collection to automated decisions

Aggregation

Data is often available in different formats, databases and - for example in the embedded or IoT sector - different devices. The first task is therefore to collect and merge all relevant data.

Pre-Processing

Unifying the data format and cleaning up artifacts to enable processing. This optimizes the potential use of the data.

Data Intelligence

Preparation and visualization of the data allow users to draw conclusions about relationships. The information content of the data is visualized in an optimal way.

Artificial Intelligence

Powerful algorithms enable automated decision-making and promise high performance. The information content of the data is used in an automated way to increase the efficiency of your processes.

Data strategies

As a service provider with over 20 years of experience in embedded software development, we offer you the development of a data strategy in heterogeneous environments. This involves identifying data sources, defining data types and finally aggregating and visualising data. This already results in added value in the uniform, clear collection of all data relevant to your application.

AI modelling

Whether for classification, process optimisation, decision-making in dynamic systems or as a recommendation system: We support you in selecting suitable AI applications for your use case. Creation, optimisation and use of the AI models from a single source.

Cloud connection

Collecting data and creating AI models is easily ported to cloud environments. The centralised architecture simplifies data collection and unifies AI modelling. Simpler workflows and more efficient solutions are the result. We support you with data migration and processing in cloud solutions.

Federated and Distributed AI

Whether federated AI, in which partial models are generated on different devices, or the roll out of centrally created models to embedded systems: With our experience, we support you in the selection of the appropriate tools, especially in the embedded and IoT sector.