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semantha® named AI champion!

Artificial Intelligence (AI) is one of the key technologies of the future and millions are being invested in this field to advance research and development in the hope of solving global societal challenges. Baden-Württemberg is the state of tinkerers and inventors and spearheads this field not only nationally, but across Europe and worldwide. 

That is why “KI Champion Baden-Württemberg” (AI champion Baden-Württemberg) is a very significant award for us. It is a great motivation for thingsTHINKING to continue researching the field of language processing and AI and thus to keep evolving our innovative software semantha. We would like to take this opportunity to thank the entire team and all supporters who believed in thingsTHINKING and semantha from the very start. 

Artificial Intelligence is gaining interest, recognition and importance in more and more industries. The initial skepticism about the technology, which supposedly spreads fear, terror, and challenges employees’ jobs, is gradually flattening out – and rightly so. semantha and other AIs are an enormous support for employees and take away tiresome and recurring tasks. Thus, the specialists can take care of the value-adding tasks in a process. 

The company: The FORBES magazine describes thingsTHINKING GmbH from Karlsruhe with the words “brain with artificial intelligence”. After more than 14 years of research in the field of language processing and AI, we founded thingsTHINKING GmbH in 2017. 

We serve customers in various industries, e.g. automotive, chemicals, insurance, legal, tax & auditing. Our platform semantha is able to compare text on the meaning level and does not stop at a word-by-word comparison.

Awards such as the “CODE_n Award – Industry Disruptor”, the award for “100 Orte für Industrie 4.0 in Baden-Württemberg” and most recently the “KI Champion Baden-Württemberg” confirm that semantha is an advanced technology with huge potential for document-driven processes.. 

The technology: semantha processes unstructured documents roughly in three consecutive steps: 

Step 1 determines semantic similarities between text passages. 

Step 2 extracts data points from documents and thereby structures them. 

Step 3 uses the data points to draw logical conclusions. 

Various AI elements are used in combination for this purpose. We are convinced that many use cases cannot be solved with just one AI element, but that it is the combination of different AI ingredients that leads to success.

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