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Erasing AI Hallucinations from High-Stakes Business

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Abstract

The rapid integration of Large Language Models (LLMs) into enterprise systems has exposed critical operational vulnerabilities, primarily driven by the tendency of these models to prioritize linguistic plausibility over factual accuracy. In high-stakes domains such as finance, healthcare, and law, the resulting "AI hallucinations"—convincing yet entirely fabricated textual and numerical outputs—pose severe financial, legal, and compliance risks. This paper examines the architectural root causes of these computational errors, contrastingly mapping them against biological human hallucinations. To address these systemic defects, this paper introduces the framework behind ELIZA, developed by SigmaCore.AI as the world’s first LLM-augmented application for Financial Planning and Analysis (FP&A) and business forecasting. By decoupling semantic processing from deterministic calculation, the ELIZA architecture achieves an operational standard of zero numerical hallucinations and zero factual discrepancies against extant corporate records. Ultimately, this study outlines how isolating and neutralizing numerical, factual, textual, and linguistic errors allows enterprise leaders to safely deploy automated intelligence within high-risk environments.