Solutions · External AI Misrepresentation
External AI Misrepresentation Audit and Monitoring
Public AI engines describe your institution to customers, regulators, and the market — and they can get it wrong. Lawnise audits how public AI represents you today against your approved evidence, then monitors material changes over time.
The second of two scopes we verify: the public AI that represents you (this page), alongside the AI you deploy.
The problem
When public AI misrepresents your institution, the exposure is yours
Customers ask public AI engines what your products cover, how your service compares, and whether you are trustworthy — and act on the answer. Those engines can get it wrong: when an answer misstates a regulated term, a rate, or a fact about your institution, it may influence how the market understands your institution. You cannot correct what you have not measured against what is actually true.
The baseline audit
Audit — a point-in-time baseline across the public engines
The audit is a point-in-time, multi-engine baseline: Lawnise collects how the public AI engines it supports answer questions about your institution, and checks each answer against your approved evidence — rate cards, product disclosures, regulatory filings, public statements. You get a reviewable record of what each engine said, where it diverged from your approved evidence, and the supported divergences and representation or reputational issues for review. A one-time engagement — it establishes the baseline; it does not recur on its own.
Audit
Point-in-time baseline
A multi-engine baseline, checked against your approved evidence. Establishes where each engine diverges today.
One-time engagement · does not recur on its own
Monitoring
Governed re-observation
Re-checks the same questions on an agreed cadence against your current, versioned approved evidence.
Separate, continuing engagement
Ongoing monitoring
Monitoring — governed re-observation against that baseline
Monitoring is a separate, continuing engagement. On an agreed cadence, Lawnise re-checks the same questions across the public engines — against your current approved evidence, versioned as your institution's facts change — and flags changes against the materiality criteria agreed for the engagement, with the evidence behind each. What is flagged escalates into review and remediation, so a divergence can be reviewed with provenance rather than relying only on customer discovery. It is governed re-observation against a versioned baseline, not a live score; when your approved evidence changes, the baseline is re-established.
What you get
A reviewable evidence record — audit and drift
The reviewable record can link a supported finding to the question, the engine's answer, the applicable approved evidence, and the divergence. The audit gives you the baseline; monitoring gives you the change from it, each with the evidence behind it — a record you can take into brand, legal, and risk review and remediation.
Each link is drawn per supported finding — audit baseline and monitoring drift alike.
Who it's for
Built for brand, legal, and risk
The teams accountable for how the institution is represented — brand and communications, legal, and risk — who need evidence of what public AI says about you and how it changes, not a marketing signal.
Start with what fits.
Establish the point-in-time baseline first, or set up governed re-observation against a baseline you already have.
Scoped access for regulated teams — we confirm the verification scope first.