AI visibility changes by market, language, persona, and competitor set.

Global AI visibility scores are too blunt for local clients. VectorGap tests how ChatGPT, Claude, Gemini, Perplexity, Grok, Mistral, and DeepSeek compare a brand inside the exact market where the client sells: country, city or local area, language, industry, buyer persona, direct competitors, and prompt intent. Localized targeting only matters when it becomes a report, a retest, and recurring delivery capacity.

Inspect the localized report proof Review proof before payment

Country, city or local-area targeting instead of one global score.
Language, industry, buyer persona, and prompt intent preserved for each audit.
Direct competitor set attached to the market where the client sells.
Prompt-level answer evidence, source gaps, remediation missions, retests, and exports.

standard providers

LLM prompts

Preference prompts

0-100

Generative Brand Index

Target context

Market → language → persona

Each audit keeps the commercial context that generic dashboards flatten: local area, language, industry, buyer persona, competitor set, and search intent.

Evidence chain

Prompt → provider → answer → source

Agencies inspect answer excerpts, cited sources, competitor preference, and missing proof before turning the finding into client work.

Client output

Mission → retest → report

Localized gaps become remediation missions, repeated retests, and reports the client can understand by market.

What is a localized AI visibility audit?

A localized AI visibility audit tests how AI systems describe, cite, recommend, omit, and compare a brand inside a specific market context. VectorGap runs prompts across the configured 7-LLM panel and evaluates answers by market, language, buyer persona, industry, and direct competitor set so agencies can see the gap their client actually competes in.

Provider coverage

Run the same localized target across ChatGPT, Claude, Gemini, Perplexity, Grok, Mistral, and DeepSeek.

7-provider panel
Provider-by-provider answers
Provider variance

Commercial context

Preserve the market details that decide whether the audit is usable for client work.

Country or local area
Language and industry
Buyer persona and intent

Competitive context

Attach the competitors the client actually loses to in that market, not generic category leaders.

Direct competitor set
Preference risk
Recommendation reasons

Why do AI answers change by market, language, persona, and competitors?

Models infer different vendor shortlists depending on region, source availability, phrasing, language, and query intent. A global score hides the local answer that the buyer actually sees.

Market changes sources

Regional directories, local proof pages, reviews, and third-party mentions can change which brand looks credible.

Language changes retrieval

The same category prompt can surface different sources, phrasing, and competitors when the buyer asks in another language.

Persona changes criteria

Executives, practitioners, procurement buyers, and local evaluators ask different questions and trigger different recommendation logic.

What does the agency receive?

The output is not a generic visibility chart. It is an evidence chain an agency can use to sell the next action: answer excerpts, source gaps, competitor preference, remediation missions, retest targets, and a client-ready report.

Audit evidence

Prompt-level answer excerpts, provider-by-provider comparison, competitor ranking, and source/citation gaps.

Remediation plan

A prioritized mission queue for proof blocks, entity facts, source cleanup, comparison pages, schema, and extractability fixes.

Report path

Retest plan and client-ready export so the agency can show what changed in the exact market being sold.

How agencies sell localized AI visibility audits

Localized audits create a concrete service line because the finding is tied to where the client sells. The agency can sell a baseline, scope a fix sprint, then retest the same market monthly.

Local baseline audit

$497–$1,500 for a focused market-language baseline with competitor context and first fixes.

Open page

Remediation sprint

$2,000–$7,500 for source, proof, schema, entity, and page fixes tied to the audit gaps.

Open page

Monthly retest/report

$500–$2,500 for repeated provider checks and client-ready movement reporting.

Open page

Localized baseline → proof → Agency OS

Localized targeting only matters when it becomes a report, a retest, and recurring delivery capacity. The buyer should inspect how market, language, persona, competitor, provider, prompt, and source context appear in the report before moving into pricing proof and Agency OS portfolio capacity.

Inspect localized report proof

Show the agency how localized answer excerpts, competitor pressure, source gaps, remediation missions, and same-target retests become a client-ready report.

Open page

Review proof before payment

Use pricing proof-before-payment to connect localized audit value to Generative Brand Index, Preference evidence, Mission Control actions, and retest plans.

Open page

Scale localized delivery

Agency OS gives the team recurring capacity for multiple markets, languages, personas, competitors, remediation missions, exports, and reports.

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
Which local buyer context changes the answer?	Country, city or local area, market, language, persona, industry, prompt intent, and competitor set are preserved as extraction context.	Run separate baselines for the markets where the client sells instead of averaging everything into a global score.
What does the agency fix first?	The localized audit exposes answer excerpts, cited sources, competitor preference, missing proof, entity gaps, schema issues, and extractability blockers by market.	Turn the largest local gap into a remediation mission for source repair, proof blocks, entity facts, comparison pages, or schema/FAQ cleanup.
How does local reporting become defensible?	The report path ties the first diagnostic to same-target retests and client-ready reports for the exact market-language-persona context.	Rerun the same local target after fixes and report what changed for that market, not a generic global trend.

Questions agencies ask before turning AI visibility into client work

Is a localized AI visibility audit different from rank tracking?

Yes. Rank tracking watches positions in search results. A localized AI visibility audit inspects what AI providers answer, which competitors they prefer, what sources they use, and which proof gaps explain the recommendation in a specific market.

Can VectorGap test city-level or regional competitors?

Yes. The audit context can include country, city or local area, language, buyer persona, industry, prompt intent, and the direct competitors that matter in that market.

Which LLM providers are included?

The standard configured panel covers ChatGPT, Claude, Gemini, Perplexity, Grok, Mistral, and DeepSeek.

Can agencies use this for multi-location clients?

Yes. Multi-location clients usually need separate market-language targets because AI answers can differ by region, source footprint, and local competitor set.

What happens after the first audit?

The agency reviews answer excerpts and source gaps, turns the biggest issues into remediation missions, ships the fixes, then retests the same market and exports the report.