This paper presents the principal outcome of the DaMSyM project (Data Mining: the Nicene-Constantinopolitan Symbolum), a transformer-based NLP tool for semantic analysis of key phrases and terms within religious tradition across Greek, Latin, Arabic, Old Church Slavonic, Sanskrit, and a combined Greek/Latin solution. To identify semantically significant patterns relevant to the project's primary case study - the Nicene-Constantinopolitan Creed across various translations - both rule-based and probabilistic architectural approaches have been employed. Specifically, selected Large Language Models (LLMs) were exploited to enable the tool to function across a wide range of Christian texts in the target languages. Featuring this models, DaMSym facilitates scholars with advanced semantic representations that support the identification of relationships, similarities, and deeper semantic layers within key religious terminology. Data filters and advanced research techniques can also be applied to query corpora to extract granular insights, thereby enhancing understanding of the theological and historical significance of individual passages across diverse translations and interpretations of the Nicene-Constantinopolitan Creed and related texts in the languages under consideration. The paper will present the digital specifications of the tool and its language-specific features, demonstrate its contribution to historical-religious research, and acknowledge its current limitations while outlining future development directions within the fields of Digital Humanities and Religious Studies, particularly concerning semantic and multilingual analysis.