Give every AI audit a source of truth.

Brand Knowledge stores the facts AI should know about the client so VectorGap can detect hallucinations, missing claims, contradictions, stale facts, and unsupported answers before those gaps become client-facing report claims.

Inspect grounded report proof Review proof before payment

Verified facts for pricing, products, markets, features, proof, competitors, and positioning.
Hallucination and correctness checks compare answers against the truth layer.
Missing facts become remediation opportunities instead of invisible report noise.
Entity consistency improves what AI can understand, cite, and recommend.

standard providers

LLM prompts

Preference prompts

0-100

Generative Brand Index

Audit anchor

Verified facts first

Each client-ready finding can be checked against stored brand truth before it becomes a recommendation, mission, or report claim.

Agency output

Wrong, missing, unsupported

The agency can separate hallucinated facts, missing differentiators, unsupported claims, and source contradictions instead of sending a vague visibility score.

Retest value

Truth layer stays reusable

When the brand updates pricing, markets, features, or proof, future audits and retests use the corrected source of truth.

The product is not just storage; it is audit grounding

Without verified brand facts, an AI visibility tool can count mentions but cannot reliably identify wrong, missing, contradictory, or unsupported claims.

Hallucination detection

Spot wrong pricing, fake features, stale locations, invented claims, and confused company facts.

Pricing and offer truth
Feature and capability truth
Entity and location truth

Missing claim detection

See when AI fails to mention important proof, differentiators, markets, or product capabilities the client actually has.

Differentiators
Target markets
Proof and outcomes

Contradiction checks

Find answer drift when public sources disagree with the client’s current truth.

Public source mismatch
Stale descriptions
Competitor confusion

Truth layer → report proof → Agency OS

Brand Knowledge only reduces audit risk when verified facts become report evidence, pricing proof, and recurring delivery capacity. Route truth-layer readers from grounded sample reporting into proof-before-payment pricing, then into Agency OS when they are ready to build the same truth-first workflow across client brands.

Inspect grounded report proof

Show how verified facts catch hallucinations, missing differentiators, stale claims, contradiction risk, and unsupported recommendations before the agency sends a client-ready report.

Open page

Reduce payment risk

Pricing proof-before-payment lets the agency inspect how Brand Knowledge supports the GBI, audit evidence, Mission Control actions, retests, and export shape before it buys the operating plan.

Open page

Buy the operating capacity

Agency OS turns the truth layer into recurring brand setup, hallucination checks, source cleanup missions, comparable retests, and client-ready reports across the portfolio.

Open page

Which evidence does AI need before it can reuse the page?

This table turns the page into a structured extraction target: the buyer question, the evidence an AI system can read, and the action an agency can sell or execute next.

Buyer question	What AI can extract	Agency action
What facts should AI repeat about the client?	Products, pricing, markets, categories, claims, proof, competitors, and source URLs are stored as audit context instead of scattered notes.	Build the client truth layer before running the baseline, then use it to challenge weak or wrong provider answers.
Which important claims are missing from answers?	Provider answers can be compared against differentiators, proof points, markets, and service facts the brand has already verified.	Turn missing claims into page updates, proof blocks, FAQ/schema additions, and retest targets.
Where does public source drift create risk?	Contradictions between current brand truth and public descriptions become visible evidence instead of hidden report noise.	Prioritize source cleanup, company-facts updates, and client-ready explanation before the next audit.

Questions agencies ask before turning AI visibility into client work

Why does Brand Knowledge matter for an AI visibility audit?

Without a verified truth layer, an audit can show what AI said but cannot reliably judge whether the answer is wrong, incomplete, stale, or unsupported. Brand Knowledge gives the agency the reference point for corrections and retests.

Does Brand Knowledge replace public-source work?

No. It defines the truth the audit should protect. The agency still needs public pages, sources, schema, and proof blocks that make those facts extractable and credible to AI systems.