Modernising historical literature into contemporary language is a form of diachronic intralingual translation that supports access to written cultural heritage. For low-resource languages such as Slovene, this task is hindered by orthographic, lexical and syntactic shifts, as well as the scarcity of parallel data. We present a two-stage pipeline that combines a fine-tuned No Language Left Behind (NLLB) model with Claude Opus 4.8 post-editing for the modernisation of 16th-century Slovene literature, retaining archaic words and phrases while normalising the alphabet, orthography and grammar. Using Jurij Dalmatin’s 1584 Bible and its 2017 modernised edition, we constructed an aligned parallel corpus of 14,876 sentence and clause-level pairs through Bohorič-to-Gaj normalisation and LaBSE-based embedding alignment. The hybrid pipeline achieved the best overall scores, reaching BLEU 45.78, CHRF 67.68 and METEOR 71.72, with TER 43.25 and CER 34.00. Its gains over the standalone LLM baseline were large and statistically significant across all the metrics, while its improvement over the fine-tuned NLLB model was smaller and significant mainly for overlap-based measures. We applied the pipeline further to Tulščak’s Kerszhanske leipe molitve (1579), producing the first preliminary modernisation of the earliest known Slovene prayer book assessed qualitatively and tested the generalisation on additional 16th-century texts, including an out-of-domain legal text. The results demonstrate that combining task-specific neural machine translation with controlled LLM post-editing offers a practical strategy for modernising low-resource historical texts and contributes a reusable methodology for digital cultural heritage preservation.