Large language models increasingly translate, summarize, and explain cultural traditions they have not inherited, raising a question more fundamental than accuracy: can a machine preserve a civilization’s meanings without absorbing them into dominant semantic regimes? This study introduces semantic sovereignty as a critical framework for auditing how large language models mediate Persianate and Shi‘i meaning across languages. Rather than treating translation quality as a matter of lexical equivalence, the study asks whether culturally dense concepts remain intelligible within their own poetic, theological, and historical horizons when processed by multilingual AI systems. Using a cross-lingual mixed-method design, it examines model outputs across four high-pressure domains: Rumi’s mystical semantics, Hafez’s lyric ambiguity, Ferdowsi’s epic-political vocabulary, and Shi‘i hermeneutic concepts such as taʾwīl, velāyat, ijtihād, and ʿismah. The analysis combines comparative prompting, translation-chain testing, expert annotation, and qualitative coding of semantic displacement. Findings show that LLMs often generate fluent and plausible responses while weakening the interpretive structures that make these texts meaningful. Rumi is frequently psychologized into global spirituality; Hafez is over-clarified through premature closure of ambiguity; Ferdowsi is depoliticized into ethical leadership; and Shi‘i concepts are flattened into generic religious vocabulary. Expert-context prompting improves semantic preservation by supplying models with interpretive constraints, yet it does not eliminate displacement. The study therefore argues that multilingual AI must be evaluated not only for linguistic performance, benchmark accuracy, or cultural bias, but for hermeneutic accountability: its capacity to preserve metaphor, doctrine, ambiguity, register, authority, and civilizational memory. By positioning Persianate meaning as a test case for AI evaluation, the article contributes to debates on digital humanities, translation studies, cultural sovereignty, and responsible artificial intelligence in an increasingly automated interpretive world. It also suggests that future audits should involve domain experts, historically grounded corpora, and evaluation criteria sensitive to local traditions of interpretation and authority too.