Persona-specific prompts
Test how executives, buyers, teams, journalists, analysts, and local prospects ask the same market question differently.
What do ChatGPT, Claude, Gemini, Perplexity, Grok, Mistral, and DeepSeek say about your client brand?
Use Perception diagnostics as the evidence layer for agency GEO work. The goal is to find where the recommendation or citation gap is real, then turn it into Mission Control work, a same-target retest, and a client-ready report.
What agencies can prove with this module
Run targeted and ultra-targeted perception checks without averaging away local or persona-specific signals.
Review recommendation language, answer accuracy, sentiment, citation context, and source proof per provider.
Explain how the same client is framed differently by market, language, or buying committee role.
Use same-target retests to prove whether content, source, and proof updates changed the answer.
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
Choose the client target
Set the buyer persona, market, language, prompt family, and competitor set before the audit runs. The point is to match the client conversation, not create a generic score.
02
Read the signal in context
Review provider-level evidence, competitor position, citations, public proof, and source gaps for that exact target.
03
Convert the gap into agency work
Package the diagnosis into content, source, entity, technical, and proof actions the client can approve.
04
Re-test and retain
Run the same target again after fixes ship so the agency can show movement in the market and buyer context that originally mattered, then package that proof in client-ready reports.
Targeting layer
Market + persona + language
Agency output
Client-safe action plan
Retention loop
Repeatable target history
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 stronger client diagnosis
The client sees the exact AI-answer context where they lose preference, perception, or share instead of arguing over a generic visibility number.
A cleaner remediation offer
The agency can scope fix sprints around source strength, public facts, content structure, entity clarity, and proof pages.
A measurable retainer story
Follow-up audits show whether shipped fixes improved the same target that exposed the original gap.
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.