Reliable use of large language models in religious settings requires more than fluent text, since a generated answer may misquote Qur’anic verses or Hadiths, assign them to the wrong source, or provide an inaccurate narrator chain. This paper presents the Taqwatech systems for Subtasks 1 and 2 of IslamicEval 2026. For Subtask 1, we fine-tune CAMeLBERT-Mix as a BIO sequence-labeling model to detect Ayah, Hadith matn, isnad, and claimed-source spans. The predicted token labels are then converted into the character offsets required by the task. For Subtask 2, we develop a corpus-grounded verification system that combines Arabic normalization, lexical retrieval, character-level TF–IDF, Qur’anic verse matching, source validation, and narrator matching. On the official test set, our systems achieved a character-level macro-F1 score of 0.9683 for Subtask 1 and a macro accuracy of 0.9050 for Subtask 2. The results show strong performance in span detection and Qur’anic citation verification, while isnad verification remains the main area for improvement.