Persona-specific prompts
Test how executives, buyers, teams, journalists, analysts, and local prospects ask the same market question differently.
How do AI systems describe competitors vs your client brand?
Use competitive intelligence as the agency layer for explaining why competitors get recommended first, how their source proof differs, and where the client brand needs a sharper public truth layer.
What agencies can prove with this module
Audit prompts by buyer persona instead of treating every AI answer as one generic user intent.
Segment visibility by target market and language so agencies can explain local AI-market reality.
Connect the score to source, competitor, and extractability gaps that the client can act on.
The goal is to understand why a competitor gets surfaced first, then decide whether the baseline audit is the right next commercial step.
For current packaging, recurring delivery centers on Agency OS: Consultant for focused client work, Agency OS for 10 Brand Brains with MCP and webhooks, and Enterprise Governance for larger programs.
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 local AI-market question
Pick the persona, geography, language, and prompt family that match how the client actually sells. A Belgian agency client, a French buyer, and an English-language analyst prompt should not be averaged into one vague score.
02
Run the comparison layer
Measure the client against competitors, source evidence, sentiment, factual accuracy, and recommendation behavior across the selected market context.
03
Translate gaps into fixes
Separate missing public truth, weak citation sources, poor content structure, and competitor narrative pressure so the agency can prescribe the next concrete work package.
04
Repeat and prove movement
Use follow-up audits to show whether the client improved in the same persona, market, and language context that originally exposed the gap.
Context layer
Persona + market + language
Agency output
Local AI market story
Client proof
Repeatable audit 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 clearer market diagnosis
The client sees where AI perception changes by country, city, audience, or language instead of arguing over one generic visibility number.
A stronger commercial narrative
The agency can sell market-specific GEO work: local proof pages, localized source building, persona-specific content, and multilingual prompt coverage.
A better retention loop
Repeat audits create a before-and-after story around the exact market context the client cares about.
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.