Persona-specific prompts
Test how executives, buyers, teams, journalists, analysts, and local prospects ask the same market question differently.
Is AI misstating facts about your client brand?
Use hallucination detection as the agency diagnostic layer for catching wrong pricing, phantom features, stale claims, and competitor confusion before clients build a remediation plan on top of bad AI answers.
What agencies can prove with this module
Compare AI claims against the client facts your agency can verify: pricing, features, markets, proof, positioning, and competitors.
Separate factual hallucinations from missing source material, stale public pages, and competitor narrative pressure.
Document the prompts and providers that repeat each wrong claim so the client can see the evidence behind the risk.
Start with the baseline audit when hallucination risk needs a proof-backed benchmark before remediation work starts.
Persona + market location + prompt language in one AI audit layer
Use this as the feature your strategist can take into a client conversation. A generic AI visibility score is too blunt for retained agency work. VectorGap shows how model perception changes by buyer persona, market, and prompt language, then turns the difference into localized proof, page, and retest work.
Market-local visibility
Separate Belgium, France, UK, US, or city-level AI perception instead of averaging every client into one global answer.
Prompting language control
Compare English, French, Dutch, German, Spanish, and other prompt languages to catch visibility gaps hidden by English-only audits.
Local AI market narrative
Help clients understand not just if they are visible, but how AI describes them to each local buyer segment.
How this feature fits into agency delivery
Every feature follows the same agency delivery loop: diagnose the AI-market signal, explain the gap, prioritize the fix, and give the client proof they can understand after the retest.
01
Define the claim set
List the client facts AI must get right: offers, pricing, features, locations, proof points, competitor differences, and claims that carry sales or legal risk.
02
Run the fact-checking layer
Compare AI answers against that source of truth and flag wrong pricing, phantom features, stale proof, missing caveats, and confused competitor attribution.
03
Decide the next proof step
The point of this route is to understand those failure modes and decide when the baseline audit should become the next proof-backed remediation step.
04
Retest after fixes
After the agency ships cleaner facts, source pages, and citation paths, repeat the same prompts to show whether the hallucination risk moved.
Fact layer
Client truth source
Agency output
Hallucination triage
Next step
Baseline audit
What should an agency extract from this feature?
A feature page should make the buyer decision extractable: what the module proves, what context matters, and what action the agency should sell after the signal appears.
Buyer question
What AI can extract
Agency action
What does this module help an agency prove?
The page exposes the module role, target context, proof outputs, workflow steps, and the baseline-audit path instead of a generic feature claim.
Use the module to explain the client gap, then connect the finding to a scoped remediation mission the client can approve.
Which context should be preserved before the audit runs?
AI can extract that VectorGap keeps buyer persona, market, prompt language, competitor set, provider answer, and evidence context together.
Set the same market, language, persona, industry, and competitor frame before comparing providers or exporting a report.
How does the feature become recurring client work?
The page connects diagnosis, source/proof fixes, Mission Control work, same-target retests, and client-visible reporting.
Package the first fix sprint, attach expected evidence, retest the same target after shipping, and report movement with excerpts the client can inspect.
What the client understands after this audit
A cleaner client conversation
The client sees the exact AI answer that misstated the brand, the fact it contradicted, and why that matters commercially.
A safer remediation brief
The agency can scope fixes around source-of-truth pages, pricing clarity, product facts, competitor disambiguation, and retesting.
A baseline-first handoff
When the issue is bigger than one isolated claim, the page sends the client into the agency baseline audit instead of a generic free-audit funnel.
Turn this into client-visible GEO work
The fastest buying path is proof the client can approve: show the AI-market gap, inspect the prompts and competitors behind it, scope the remediation work, and retest the same target after your team ships.
How agencies should use this feature
Is this feature a standalone dashboard or part of a delivery workflow?
It is part of the VectorGap delivery loop: diagnose an AI-answer gap, explain the evidence behind it, create remediation work, retest the same target, and export client-ready proof.
Can agencies use this for localized client work?
Yes. The feature detail pages emphasize market, language, persona, and competitor context because agencies need to sell work around the exact buyer segment where the client is losing visibility or preference.
What should the agency show the client after using this feature?
Show the AI-answer evidence, the source or entity gap, the remediation mission, and the retest result. The client should understand what changed and why the next action matters.