SigmaCore.ai released findings from a benchmark study evaluating Large Language Model calculation reliability in commercial financial deployments, titled "Can We Trust the AI to Do the Math?" Across 3,400 financial test cases, generative LLMs using direct computation achieved 63.5% exact precision, augmented LLMs with code execution enabled achieved 94.7%, and SigmaCore's Eliza deterministic computation engine achieved 100%. The study observed 10,200 total experimental executions assessing 34 standard commercial financial calculations, including loan amortization payment schedules, three-year financial projections, and break-even financial assessments used in Small Business Administration application streams, executed across 100 randomized market scenarios. The study found that augmenting standard LLMs with programmatic code execution reduced minor math slipups but introduced deep logic vulnerabilities due to model confusion regarding correct formula selection; in two retail financial scenarios, accuracy plummeted to 18% when code tools were active because models mistakenly generated flawed financial formulas with high structural confidence. The data points to no reliable proxy metrics—such as answer length or operational difficulty—capable of warning users of an error, with precision degrading sharply as arithmetic complexity advanced. SigmaCore.ai protects enterprise accuracy across three core products: Eliza, Lex, and Azile.