Governance intelligence for the era of public AI models

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.

We put high-intent questions about Malaysia's PIDM/TIPS insurance protection scheme to a set of public AI systems and checked each answer against PIDM's own official page. Several tested answers tracked the everyday insurer details correctly; the misses landed on the protection facts: the RM500,000 limit (which aggregates across same-life policies with one insurer) told as per-policy, so three policies read as RM1.5M; and the life/death-benefit limit told as RM250,000 — the bank deposit-insurance figure. A preliminary, single-month reading of a risk one step outside most governance frameworks.

We put high-intent retail questions about Malaysian banks to a set of public AI systems and checked each answer against the bank's own published service charter. The pattern was degradation, not invention: a 3-working-day card commitment told as 2–3 weeks; a 5-day mortgage as ~30 days; a 14-calendar-day complaint decision as 20 working days. A preliminary, single-month reading of a risk one step outside most governance frameworks.

We put high-intent questions about Singapore's insurance schemes to a set of public AI systems and checked each answer against the official record. Other tested systems were often correct; on three rules one system each missed where a consumer acts — CareShield Life's 3-of-6-ADL trigger said as 1; FIDReC's S$150,000 limit said as S$500,000; FIDReC-NIMA's non-injury, sub-S$3,000 scope described as broadly available. A preliminary, single-month reading of a risk one step outside most governance frameworks.

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.

We put high-intent retail questions about Singapore banks to a set of public AI systems and checked each answer against the bank's own published facts. Many aligned with the published record; a recurring few didn't — and they missed by a lot, where a customer acts: a 28.5% cash-advance rate quoted below it, a 27.9% card rate quoted at 15–18%, a two-business-day complaint window quoted as five. A preliminary, single-month reading of a risk that lives one step outside most governance frameworks.