Independent verification for the AI you deploy and the public AI that represents you.
Covering banking and insurance in Malaysia and Singapore at launch scope.
Access is scoped for regulated teams after the right governance surface is confirmed.
Regulated enterprises are deploying their own AI — assistants, copilots, decision agents — into work that carries the institution's name. At the same time, public AI engines describe that same enterprise to customers and regulators. Both surfaces speak for the institution, and when either gets it wrong, the institution bears the consequences. Lawnise verifies each against your approved evidence.
Lawnise builds the independent AI verification infrastructure that closes the gap.
We call this AI TRiSM for enterprises — Trust, Risk, and Security Management across the AI a regulated enterprise deploys and the public AI that represents it.
Lawnise is the verification layer: continuous scanning of public AI, connector- and intake-driven verification of the AI you deploy, three-engine risk scoring across both, and a ledger of immutable, hash-bound, evidence-preserved Truth Packets that turns each verified answer into a durable record.
Lawnise scans the major public AI engines on a published cadence. Every response is captured, deduplicated, context-snapshotted, and routed into the risk pipeline.
Conversational AI engines
API-based scanning of the major conversational AI engines, on a published cadence. Engine-set defined — expanded as engines stabilize, contracted as they retire.
Search-augmented + Live AI surfaces
Live conversational scanners cover surfaces without stable APIs — search-augmented answer panels and live web AI. Same observation schema, same risk pipeline.
Multi-jurisdiction coverage
Launch scope: Malaysia + Singapore. Region-aware prompt sets, locale-specific reference data, sector-specific compliance rule packs.
Continuous + scheduled scans
Scheduled brand-check runs plus event-triggered scans on regulatory or knowledge-base updates. Every observation lands in the response ledger with a replayable context snapshot.
Every observation runs through three independent risk engines in parallel — accuracy, compliance, and reputation. Findings are recorded, evidence-anchored, and routed to analyst review.
ACCURACY
Fact verification adjudicates each claim — from your own AI's answers and from public AI — against your approved knowledge base. Confidence-scored contradictions, verdict reason codes, replayable input snapshots — facts adjudicated, not guessed.
Outputs → verdict + confidence
COMPLIANCE
Compliance rule packs detect breach conditions in real time. Logic triggers, mandate failures, section references — every material finding can be escalated into a forensic Truth Packet ready for auditor review.
Outputs → rule pack + section ref
REPUTATION
Reputation analysis scores AI-described sentiment against your brand attributes, competitor mentions, and toxic-entity exposure — flagged before the framing spreads.
Outputs → sentiment + entity flags
Evidence infrastructure
Lawnise anchors every risk finding in an immutable Truth Packet — hash-bound, evidence-preserved, and replayable, traceable from AI answer to verdict. This is what regulatory teams export to auditors.
AI Answer
Raw response captured from an AI engine — the AI you deploy or a public one — with a full context snapshot for replay.
Claim Extracted
Atomic factual claims pulled from the response and routed to fact verification.
Evidence Bundle
Brand Knowledge Base sources retrieved and hash-bound to the claim.
Risk Verdict
Three engines render verdicts in parallel. Status pill is enum-locked.
Truth Packet
Immutable, hash-bound, evidence-preserved, and replayable adjudication record. Audit-ready, exportable.
Verify the AI you deploy and the public AI that represents you — and prove what is wrong with evidence, before an inaccurate answer becomes customer, regulatory, or reputational risk.
Lawnise is built for procurement-grade governance — not just monitoring. Audit trails, evidence hashing, compliance rule packs, analyst workflows, and an append-only response ledger. The buyers' security review starts with data handling and ends with audit readiness.
AUDIT-GRADE
Lawnise separates customer-side configuration from benchmark reference data, and publishes its data-handling posture for procurement review. Retention modes for source-governance document review: full / ephemeral / immediate.
PROCUREMENT-GRADE
The full subprocessor list is public — required by GDPR, MY PDPA, and SG PDPA. The Trust Center publishes data-handling, retention, and incident-response posture. See the full list before you sign.
The launch scope covers banking and insurance in Malaysia and Singapore. The platform's architecture extends to other regulated sectors — and to public-AI verification beyond financial services — but the Trust Index methodology, sector measurement, and reference data are built one wedge at a time. Sector coverage expands when the data and the review discipline catch up, not when the marketing calendar wants it to.
Buyers, regulators, and journalists ask public AI about your products, your eligibility rules, and your incident history. The answers shape decisions you do not control.
Read how independent AI verification covers bankingCustomers ask AI what your policies cover, how to claim, and how you compare. Inaccurate answers move quote requests to whichever insurer the AI describes more clearly.
Read how independent AI verification covers insuranceLaunch focus: banking and insurance in MY + SG. Additional regulated sectors are sequenced through the roadmap. Ask about the roadmap.
Authority surface
The Lawnise Trust Index is Lawnise's research methodology for measuring how accurately public AI models describe regulated enterprises. Scores are produced under a documented, versioned methodology, with every score traceable to evidence in the underlying ledger. Lawnise applies the methodology for internal measurement, market education, and research deliverables. It anchors the Independent AI Truth Jurisdiction Infrastructure (InATJI) — Lawnise's long-term doctrine that AI accuracy about real institutions is a measurable public good.
See the Lawnise Trust Index methodology for the full scoring framework.
Methodology, evidence ledger, and right-to-reply policy are public.
Subscribe — Lawnise research updates
Start with a scoped pilot or briefing. Lawnise configures access around your governance surface, markets, and evidence needs.
Scoped access for regulated teams.
For procurement teams evaluating Enterprise. 30-minute working session.