In a head-to-head benchmark, Archer® released results showing its purpose-built AI, Archer Evolv™, outperformed a leading general-purpose large language model (LLM) on regulatory change management. The test focused on determining publication, effective and comment-close dates of regulatory documents across six jurisdictions. The general-purpose LLM was wrong 56% of the time on the same 55 documents, and of the answers it rated high confidence, 35% were still wrong. With Archer Evolv, more than 95% of determinations are verified outright, and the rest are routed to an expert before use; not a single wrong date reached production. The general-purpose process averaged about four seconds per response. Archer reported 95% verified accuracy, 80x faster speed, and 92% lower cost. Kayvan Alikhani, Chief Product and Technology Officer of Archer, stated that Archer's purpose-built AI verified more than 95% of determinations in real time, providing a foundation to scale AI agents without losing control.