1
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.
Feature / Agency GEO operations
Ultra-targeted LLM Perception
Track how AI providers describe the client brand for the target that matters: country, language, buyer persona, prompt intent, and competitor set. Agencies can stop reporting vague global perception and start explaining the exact buyer context where the brand wins or loses.
Operational role
What this module helps an agency prove
Run targeted and ultra-targeted perception checks without averaging away local or persona-specific signals.
Review visibility, accuracy, sentiment, citation context, and recommendation language per provider.
Explain how the same client is framed differently by market, language, or buying committee role.
Use repeat audits to prove whether content, source, and proof updates changed the answer.
New agency advantage
Persona + market location + prompt language in one AI audit layer
This is the feature agencies should lead with. A generic AI visibility score is too blunt for local client work. VectorGap lets the agency explain how AI perception changes by buyer persona, target market, and prompting language, which turns the audit into a local market intelligence product instead of a flat dashboard.
Persona-specific prompts
Test how executives, buyers, teams, journalists, analysts, and local prospects ask the same market question differently.
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.
Workflow
How this feature fits into agency delivery
Each feature page now uses the same product story: diagnose the AI-market signal, explain the local gap, prioritize the fix, and give the client proof they can understand.
2
Read the signal in context
Review provider-level evidence, competitor position, citations, public proof, and source gaps for that exact target.
3
Convert the gap into agency work
Package the diagnosis into content, source, entity, technical, and proof actions the client can approve.
4
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.
Targeting layer
Market + persona + language
Agency output
Client-safe action plan
Retention loop
Repeatable target history
Agency outcomes
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 the client baseline
The fastest buying path is clear product value: show the local AI-market gap, inspect the prompts and competitors behind it, and use the feature set as the remediation workflow.