Enterprise risk layer
AI Perception Intelligence for Enterprise Risk, Credit & Compliance
Brands are the door. Enterprise risk is the room.
Large language models are already the first research step in screening, KYB review, and credit analysis. Different models return materially different assessments of the same company. That divergence is currently invisible and unpriced.
Live report surface
VeritasScore · multi-model distribution
Northline Analytics · illustrative fixture on the real UI. Not a screenshot.
Score by LLM model
620Overall · 300–870
The blind spot in existing diligence budgets
Risk and compliance teams already pay for data rooms, KYB vendors, and bureau files. None of those stacks measure how large language models currently assess the same name. Brands are the door — that is how the signal is trained. Enterprise risk is the room: split-perception as an unpriced information risk, surfaced as a decision-ready, auditable evidence pack.
M&A · investment
M&A analysts
The data room shows the company’s version. AI shows the market’s — and the gap prices into the deal.
Open use-case →
Banks · credit
Banks & credit teams
Lend on stated data and history while an independent layer already signals where the company is heading.
Open use-case →
Compliance · onboarding
Compliance / KYB–KYC
Registries prove a company exists on paper. AI shows how it actually lives in the market — inflated, split, or phantom.
Open use-case →
What we measure
VeritasScore runs on a 300–870 scale modeled on consumer credit scoring. It decomposes monotonically into visibility, positioning, and risk deduction — not a black-box vibe. Multi-run distribution across frontier models detects split-perception. The same pipeline layers Digital Footprint, GEO, AI Focus Groups, and Narrative Review so a mention is never mistaken for a recommendation.
Data from brand analyses is the flywheel. The signal risk teams use is trained on that volume, not on a one-off chat. Citation is not recommendation. Brand-scope stays separate from narrative-scope (AI Investor Review).
Visibility
90
Brand Visibility component (max 150). Decomposable, monotonic.
Position
72
Market Position. Omission on category queries pulls this down.
Preference
22
Brand Preference from Focus Groups (max 40).
Cross-model gap
130 pts
ChatGPT 680 vs Claude/Grok 550. Near the paper’s 90th percentile (134).
Live report surface
VeritasScore · multi-model distribution
Northline Analytics · illustrative fixture on the real UI. Not a screenshot.
Score by LLM model
620Overall · 300–870
Why single-pass tools fail risk teams
A single chat answer is not a method. It has no distribution, no repeatable prompt grid, and no model–evidence pair you can file. Typical GEO tools optimize coverage for marketers. They flatten the spread that diligence, KYB, and credit need to see.
Three paths into the same evidence layer
You do not need a new budget line with a new name. You need the missing layer on work you already buy. Pick the workflow you own; the methodology does not change.
M&A · investment
M&A analysts
The data room shows the company’s version. AI shows the market’s — and the gap prices into the deal.
Open use-case →
Banks · credit
Banks & credit teams
Lend on stated data and history while an independent layer already signals where the company is heading.
Open use-case →
Compliance · onboarding
Compliance / KYB–KYC
Registries prove a company exists on paper. AI shows how it actually lives in the market — inflated, split, or phantom.
Open use-case →
Concept
Split-perception
Why models disagree, two volatility regimes, and how we surface the spread.
Explainer →Narrative
Investor Review
How models diligence a pitch deck or credit narrative — kept separate from brand-scope.
Narrative diligence →Compare
Vs the stacks you have
KYB vendors, bureau files, and marketing GEO tools — different jobs, not cheaper clones.
Comparison pages →Working paper
The method is public. The gap is measured.
Drawing on 5,000+ companies and several million model responses, the Split-Perception working paper (SSRN) puts a number on cross-model divergence: median 100 pts, 90th percentile 134 pts, retrieval volatility up to 200+ pts. Citation is not recommendation. Split-perception is unpriced information risk for M&A, KYB, and credit workflows that already start with an AI first pass.
100 pts
Median cross-model gap on the 300–870 scale (fully instrumented subset).
134 pts
90th percentile gap. Structured disagreement, not one noisy prompt.
200+ pts
Retrieval-grounded model swing on one firm across repeated runs.
Read the paper →Download PDF →Split-perception for risk teams →
Price the perception layer you already use.
Request an enterprise POC for KYB, credit, or a live deal — or run a free analysis on any public domain. Same pipeline. Decision-ready evidence.
Free analysis
Run it on any company
Public URL. Same funnel as the homepage. No card required.
Enter a public website
Multi-model · VeritasScore · ~5 minutes
See a sample dossier →FAQ
Short clarifications — positioning and proof live in the sections above.
What is AI perception intelligence for enterprise risk?+
It is an evidence layer that measures how large language models currently describe, rank, omit, or warn about a company. VeritasLinks turns that perception into an auditable VeritasScore (300–870), a multi-model distribution, and ranked remediation — so risk, credit, and diligence teams can see split-perception instead of treating a single chat answer as a finding.
How is this different from a KYB registry check or a credit bureau score?+
Registries prove legal existence and ownership. Bureau files summarize stated history. Neither measures how frontier models currently assess the same counterparty. That model–evidence pair can already be inflated, split, or thin — and it is the layer counterparties, analysts, and underwriters increasingly consult first.
What is split-perception?+
Split-perception is structured disagreement between large language models about the same company on identical questions. In the Split-Perception working paper, the median cross-model gap is 100 points on the 300–870 scale, with a 90th percentile of 134. The gap is a property of the model–evidence pair, not one noisy prompt.
Does a citation mean a model recommends the company?+
No. Citation is not recommendation. A model can retrieve a source, describe a firm accurately, and still omit it from a short list — or warn against it. VeritasLinks separates visibility, position, and risk so those are not collapsed into a single “mentioned” flag.
Where does this sit in an existing diligence budget?+
It sits beside the work you already buy: data rooms, KYB vendors, bureau pulls, and analyst memos. It does not replace them. It prices an information risk that those stacks were not built to see — how AI systems currently perceive the name under review.
Can we run a proof of concept without a new vendor program?+
Yes. Run a free analysis on any public company domain, or request an enterprise POC scoped to KYB, credit monitoring, or a live deal. The same pipeline that produces brand dossiers is the signal risk teams use.