Artificial Intelligence

Together with you, we identify the specific use cases of AI, quantify their benefits and demonstrate their feasibility.

As a long-standing expert in the field of “Artificial Intelligence”, INVENSITY offers medium-sized companies a cost-conscious and yet tailored offer for the use of AI in system and machine operation as well as in production.

As a company at the interface between engineering & IT, we understand complex physical systems and their data in detail with our >200 employees.

We have the data science know-how to identify suitable machine learning applications and evaluate them together with our customers.

INVENSITY is part of the KARLI research project, an AI research project funded by the German Federal Ministry of Economics and Climate Protection with a project volume of approximately €16 million. Together with Continental, Audi, Ford, and Fraunhofer IOSB, among others, we are working on the latest ways to transfer the theoretical potential of AI into practice.

INVENSITY is one of the consortium partners in the research project KIMORo. The project focuses on the evaluation of the development and production of an Artificially Intelligent Modular Opensource Robot with regard to technical and economic feasibility. The project is supported by funds from the state of Hesse.

The INVENSITY AI Data Value Report

The INVENSITY AI Data Value Report is a practice-oriented consulting offer for the automated analysis of your data.

Within ten days, we analyze your existing (sensor) data and provide you with concrete suggestions for the individual use of Machine Learning.

In addition to the technical feasibility, we also consider the economic added value, for example, through material or energy savings, better system availability, or quality increases.

Our price/performance ratio is attractive for medium-sized businesses because we combine customized consulting with automated data analysis to identify the most economically relevant application areas of Machine Learning.

The automated data analysis checks, on the one hand, the quality of the data (e.g., completeness and consistency) and, on the other hand, the usability of the content using different Machine Learning algorithms. In the process, more than ten approaches are evaluated. The performance of the trained models is compared with each other so that the economic viability of the AI deployment can be seriously assessed.

Data Value Chain

Digitalization means more than just recording data from the real world. Evaluating it, visualizing it and creating new added value turns data into real value creation. A targeted analysis of existing data, possible value creation and possible technical implementation is necessary to present an optimal data value chain.

Whitepaper

Event-driven cloud-based architecture for Data Centric AI development

A new research field in AI focuses on improving data quality and speeding up dataset creation from raw data. With challenges such as handling the growing volume of data in the context of IoT and machine learning, there is a need for specialized ETL tools to provide consistent updates for AI models. This whitepaper explores how an event-driven cloud-based architecture addresses these challenges and offers scalability and efficiency.

Streamlining Software Development: The Impact of Large Language Models on Unit Testing

Large Language Models (LLM) offer a swift and effective solution for high-quality software creation, covering tasks from code review to security analysis. By deploying applications within your cloud environment, data privacy remains paramount. This whitepaper explores a specific use case: leveraging an LLM cloud service A new research field in AI focuses on improving data quality and speeding up dataset creation from raw data. With challenges such as handling the growing volume of data in the context of IoT and machine learning, there is a need for specialized ETL tools to provide consistent updates for AI models. This whitepaper explores how an event-driven cloud-based architecture addresses these challenges and offers scalability and efficiency.to develop a hybrid-AI algorithm that automates unit test creation, reclaiming over 10% of developer time for more impactful tasks.

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