Today, the heterogeneity of NLP tools in relation to existing methods and the constantly growing availability of models (see Hugging Face<ref xmlns="http://www.tei-c.org/ns/1.0" n="1" target="ftn1" />) confronts various disciplines with major challenges in the daily handling of natural language processing. The spectrum of disciplines, although this is not exhaustive, ranges from biodiversity (e.g. (Lücking et al., 2021; Folk et al., 2024)), medicine (e.g. Poon et al. (2017); Redondo et al. (2019)), linguistics (e.g. Abdurakhmonova et al. (2022); Lücking et al. (2024)), all the way to the digital humanities (e.g. Brooke et al. (2015); Tasovac et al. (2023)). In parallel, the amount of available and usable corpora is also growing regularly in various areas, including, among others, corpora such as the “Collosal Clean Crawled Corpus” (C4 - (Raffel et al., 2020)), parliamentary protocols (e.g. Rauh and Schwalbach (2020); Abrami et al. (2022, 2024)), newspaper corpora (e.g. Süddeutscher Verlag (2014); New York Times (2019)), social media corpora (e.g. Dimitrov et al. (2020); Kratzke (2023)), COW (Schäfer, 2015) as well as Wikipedia (Pasternack and Roth, 2008). These are golden times for all scientific fields, as different models can be applied to the respective corpora; although in the short term this leads to non-trivial challenges in terms of a) analysis time, b) heterogeneity of (corpora) formats, c) processing input and output formats as well as d) analyzeability. These many <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">construction</hi> phases show the need for a reliable working tool that can be used without intensive training, which is available in the form of <hi xmlns="http://www.tei-c.org/ns/1.0" rend="bold">Docker Unified UIMA Interface</hi> (DUUI)<ref xmlns="http://www.tei-c.org/ns/1.0" n="2" target="ftn2" />.