Archer® today released results showing purpose-built AI beats a general-purpose large language model (LLM) on regulatory work, and it's not close. This head-to-head test compared Archer's purpose-built, vertical-specific AI and proprietary data sets against a leading general-purpose LLM, on a core compliance task: determining the publication, effective and comment-close dates of regulatory documents across six jurisdictions. A general-purpose LLM was confidently wrong 35% of the time on regulatory dates. Not a single wrong date reached production with Archer Evolv.
General-purpose LLM was wrong 56% of the time, 35% confident but wrong.
Archer Evolv achieved 95% verified accuracy with 5% expert-checked.
Archer Evolv served verified dates ~80x faster than general-purpose process.
Archer Evolv avoids 92% of inference calls through persistent knowledge base.
General-purpose LLM failed or timed out 31% of the time.
Archer serves half the Fortune 500 and 37 of top 50 global banks.
On the same 55 documents, the general-purpose LLM was wrong 56% of the time. Confidence made it worse, not better. 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. Kayvan Alikhani, Chief Product and Technology Officer of Archer, stated: "In compliance, an answer that is fast and cheap, but wrong, is worthless, and an answer you cannot trace is a liability. Archer's purpose-built AI verified more than 95% of determinations in real time. That is the foundation that lets enterprises scale AI agents without losing control of the outcome."
Per request, the general-purpose process averaged about four seconds per response within a five-second timeout. Archer Evolv served a verified date in roughly five-hundredths of a second, about 80 times faster on repeat lookups. For AI agents and analysts working at the pace of a regulatory calendar, that is the difference between keeping up and becoming the bottleneck.
A general-purpose process recomputes the answer on every request, with no memory of what it found before. Archer Evolv computes once at ingestion, verifies the result into a scalable, expert-governed knowledge base, and persists it for every future lookup at a fraction of the cost and latency. When a regulation is amended, Evolv catches the change proactively, re-verifies, and versions the updated answer. For a 500-document corpus with 12 lookups each per month, Archer Evolv avoids roughly 92% of the inference calls, a structural saving that widens as volume grows.
Archer Evolv's advantage traces to context: before any AI runs, it assesses the organization's jurisdictions, products, business units, risks and regulatory themes, so every determination is grounded in what is relevant to that enterprise. This is the difference between an answer and a defensible answer. The more agents a company deploys, the more valuable that foundation becomes.
Bill Diaz, Chief Executive Officer of Archer, stated: "The companies that win the next decade of SaaS will pair domain-specific AI with proprietary, vertical-specific context the foundation models cannot replicate. That is the moat, and it compounds. This test is the proof."
About Archer
Archer powers how the world's leading enterprises govern risk, compliance, and regulatory change. More than 1,300 organizations run on Archer, including half the Fortune 500 and 37 of the top 50 global banks. A new regulatory change lands somewhere in the world every six minutes, and agentic AI is outpacing most teams' ability to govern it. Archer's purpose-built AI is grounded in the deepest regulatory data and domain expertise in GRC, so every result traces back to its source, and every decision can be defended. Archer delivers solutions across the full range of GRC, including regulatory change management, AI risk management, regulatory intelligence, third-party risk, and IT and security risk.
