Insurance is a difficult domain for LLMs because decisions depend on evidence that is both textual and procedural: policy wording, exclusions, endorsements, medical documents, repair estimates, jurisdictional rules, and internal authority matrices. A fluent answer is worthless if it grounds itself in the wrong clause.
Classic vector retrieval is useful but insufficient for long, clause-heavy documents. Similarity can surface passages that sound plausible while missing the operative exception. A stronger architecture first identifies document structure, then retrieves the decisive section, then asks the model to reason within that evidence boundary.
For Saudi and bilingual environments, the problem compounds. Arabic and English documents may not align perfectly; regulatory phrasing may be local; operational templates may repeat language across products. Insurance AI should be evaluated on grounding quality, not just answer quality.