Evidence-backed research and briefings on how the AI enterprises deploy and the public AI that represents them behave — whether answers are accurate, what risks they create, and how teams can verify them.
We lead with analysis, ensuring every Lawnise briefing surfaces the risks, controls, and executive actions that matter most for enterprise AI deployment.
Explore our most recent research briefings on enterprise AI accuracy, risk, and verification.
Lawnise checks public AI answers about Malaysian banking. October findings concern deposit protection, complaint escalation and debt restructuring.
Lawnise checks public AI answers about Singapore insurance. October examples examine protection-scheme membership and access to customer support.
How to connect Claude, ChatGPT and other AI apps to Lawnise through MCP: setup, example requests, approvals, the tools and how your data is handled.
Lawnise checks public AI answers against Singapore banking rules. October examples concern card liability, mortgage tenure and interest-only purchase loans.
New California laws and embedded evaluators are expanding independent AI evaluation. Audits of controls still leave a question: were the answers in use checked?
Why independent AI evaluation must continue beyond frontier labs into customer-facing assistants, internal copilots and public AI answers.
Lawnise checks public AI answers about Malaysian insurance. September findings cover FMOS scope, PIDM protection and complaint routing.
Learn how internal AI validation checks selected chatbot answers against approved facts and procedures, and where it fits within AI governance.
Public AI answers about your institution and the AI you deploy are two governance surfaces. The difference is control and evidence access, and what that changes.
The FCA Board-commissioned Mills Review recommends examining how general-purpose LLMs outside the perimeter influence retail financial decisions.
EU AI Act Article 50 — the AI-transparency rules from 2 Aug 2026 — marks AI content as AI-made, but not whether it's accurate about your institution. Where the gap is.
AI governance solutions govern the AI you deploy — but not the public AI answering customers about you. Lawnise on governing that external answer-risk surface.
Is ChatGPT accurate? Trust it on general, settled knowledge — verify anything specific, current, or organisation-level. Lawnise's practical trust-vs-verify guide.
How Lawnise verifies public AI answers against official sources before turning a flagged answer into a published research finding.
A German court has provisionally enjoined Google over false statements in its AI Overview. Lawnise reads the non-final ruling as an early signal about public AI answer governance.
ChatGPT is strong on general knowledge, but specific, current facts about an organisation need verification. Lawnise explains the contextual accuracy gap.
Contextual accuracy is whether an AI answer is correct about your specific case, not merely correct in general. Lawnise explains why that distinction matters.
A cross-market Lawnise barometer: read together, our readings of Singapore banking, Singapore insurance, Malaysian banking and Malaysian insurance show one pattern — the facts firms and schemes have already published are right, and a recurring few public-AI answers restate them wrong, where a customer acts. One pattern, several markets; a growing program, not a single accuracy score.
Public AI assistants now answer customers' questions about your institution — and often get the product, price, complaint timeline, or fraud channel wrong. You can't see or edit those answers, yet the consequences land at your door. This explainer makes the case that their accuracy is a governance surface you already own, and lays out the loop for governing it.
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