What does the AI Visibility Baseline include?

The planned baseline builds the buyer-question set with you, records approval, samples a complete window across the validated provider surfaces, and delivers a first report with a prioritized fix plan. It will not be sold or described as live measurement until a credentialed provider run is recorded.

How is this different from a free AI visibility checker?

The free scanner checks page readiness; it does not query answer engines at all. The planned engagement repeats an approved question set across validated ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode provider surfaces, records the provider/model identifier, and reports each rate with its sample count and uncertainty range. Provider API output may differ from the consumer interface, and the report says so.

Can you guarantee ChatGPT will recommend my business?

No. After provider sampling is recorded, the engagement commits to a versioned method and comparable windows. Uncertainty ranges are descriptive under the sampling assumptions; repeated model outputs may not be independent, recommendation classification is heuristic, and before-and-after movement is not causal proof. Offline test output is never used as measurement.

How are the monthly scope and fee set?

After the baseline, the written scope accounts for the approved questions, answer engines, locations, competitors, and the remediation backlog. The engagement starts with a three-month term and then moves month to month. You receive the scope, cadence, decision boundaries, and fee together before deciding.

Is this just local SEO with a new name?

No. Local SEO measures visibility on search and map results; this measures whether sampled answer engines name and cite the business. The work can overlap around profiles, structured data, and citations, so that overlap is identified in the scope rather than presented as two separate fixes.

When should I expect the numbers to move?

There is no defensible fixed timetable. The first window is a baseline, not a result. Later reports show whether a rate changed beyond its uncertainty and which sources changed with it. They do not attribute calls or revenue to AI visibility without evidence that can support that claim.

Visibility measurement — AI Visibility Measurement

Scoping conversations

A planned measurement and remediation program for how configured provider answers describe and cite your business.

When buyers ask an AI, can it find you?

Planned measurement across ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode. The free technical-readiness scan is live; provider sampling is not yet live and is not sold as a measured baseline. The free scan checks one public page. It does not ask any provider whether they name the business.

Discuss the AI Visibility Baseline

Current status · Measurement not yet live

The free technical-readiness scan is live. Repeated sampling is built and tested offline. It is not a live baseline until a credentialed provider run is recorded. Offline test output is never reported as measurement.

Briefing

Some buyers now ask an assistant for a recommendation instead of working through a results page. That creates a different visibility question: whether the answer names the business, links it, and relies on accurate sources.

A single manual check cannot answer that question reliably. Answer engines can return different businesses and sources across repeated runs, so one response has no useful interval around it. The business profile, structured data, directories, and cited third-party pages also have to be compared against the business facts they describe.

The planned method records the prompt-set version, provider surface, model identifier, location, schedule, sample count, and raw response. Recommendation classification is heuristic. Uncertainty ranges are descriptive under the sampling assumptions—not proof that repeated model outputs are independent or that remediation caused a change.

A consultant cannot control a model response. The controllable work is making relevant first-party and public source information accurate, complete, and consistent.

Three ways forward

Three options. One recommendation.

Keep paying for keyword rank.

Position on a search-results page is a real metric for that channel. It does not answer whether an AI response names or cites you, so the two measurements should not be treated as interchangeable.

Check it yourself, occasionally.

Asking ChatGPT about your own business is one sample with no baseline and no comparison. It feels like measurement, produces a number, and cannot support a decision.

Measure it properly, then fix it.

After live validation: repeated samples across the validated provider registry, method details and limitations beside the rates, and remediation aimed at gaps the evidence can support.

How the engagement starts

Two scopes. One decision at a time.

Live measurement starts only after provider sampling is recorded. Until then, the public scan is the only live instrument.

AI Visibility Baseline

Bounded first step

Fixed diagnostic

One time, about one week

A planned live measurement window, first written report, and prioritized fix plan. Live measurement starts only after provider sampling is recorded.

Scope is set by

The approved question set, validated provider surfaces, locations, and competitors included in the baseline.

AI Visibility Measurement

Optional standing engagement

Standing engagement

Three-month initial term, then month to month

A measurement and remediation engagement designed to sample five configured answer-engine surfaces for approved buyer questions. The free technical-readiness scan is live; provider-backed answer sampling is not yet live and is not sold as a measured baseline.

Scope is set by

The approved question set, validated provider surfaces, locations, competitors, and remediation backlog.

Discuss the AI Visibility Baseline

AI Visibility Baseline

Commitment

One time, about one week

Scope is set by

The approved question set, validated provider surfaces, locations, and competitors included in the baseline.

Live measurement starts only after provider sampling is recorded.

Format

A question set built with you and approved before anything runs — the phrasings your buyers actually use, not the ones you wish they did
A complete sampling window across the validated ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode provider surfaces
A written report with surface, recommendation, link, and citation measures; sample counts; descriptive intervals; and the assumptions behind them
A per-surface and per-question breakdown with prompt-set version, provider/model identifiers, location, schedule, and raw samples available for review
A prioritized fix plan tied to observed source gaps, without claiming that before-and-after association proves the remediation caused the movement
A written scope recommendation for continued monitoring, including when the evidence does not justify it

Discuss the AI Visibility Baseline

The AI Visibility Baseline, offered as live measurement only after provider sampling is recorded.

This is right for you if…

You suspect the AI channel matters, accept that provider API surfaces are a controlled proxy rather than the consumer interface, and want the method and live validation boundary disclosed before a baseline is proposed.

AI Visibility Measurement

Commitment

Three-month initial term, then month to month

Scope is set by

The approved question set, validated provider surfaces, locations, competitors, and remediation backlog.

After validation: weekly sampling, a monthly written read, and scoped remediation.

Format

A written monthly report with surface, heuristic recommendation, linked-citation, and citation-share measures; sample counts; descriptive uncertainty ranges; and the sampling assumptions stated.
A per-surface and per-question breakdown across the validated provider registry, including provider/model identifiers and approved questions where you did not appear.
Google Business Profile, schema, and name-address-phone corrections tied to documented gaps.
Directory and citation work focused on third-party sources that appear in the samples or contradict the business's canonical facts.
A prior-window comparison labeled as descriptive association, not causal proof that remediation produced the movement.
A 30-minute review call each month, with raw samples available on request.

Discuss the AI Visibility Baseline

After live validation, sampling is planned weekly across configured ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode surfaces, with a written monthly report and review call.

This is right for you if…

You have a buyer-approved question set and a business-facts source of truth, and you want repeated provider-backed sampling interpreted with its methodological limits rather than a single manual response treated as a verdict.

Fit

Worth knowing before you call.

Signals this engagement may fit

Service businesses and multi-location operators that need evidence about whether AI answers name and cite them — not another keyword report.

Competitors appear in AI answers about your category and you do not.
Your Google Business Profile, schema, and directory listings disagree with each other.
Buyers arrive quoting something an assistant told them about your category — sometimes about a competitor.
You already pay for search work, but current reporting does not address the AI-answer channel.

Where this is the wrong call

Anyone who wants a guaranteed mention or placement. A consultant or measurement provider cannot control a model response.
Teams who want keyword rank tracking. That is a different measurement and a different service.
Businesses with no substantive website yet. Establish the source material first; there is little here for an engine to read.
Anyone who wants a dashboard login without a written interpretation and a prioritized remediation backlog.

Side by side

One time, or every month.

This is not an upsell decision. It is a choice between a bounded piece of work and an optional standing commitment.

Dimension	AI Visibility Baseline	AI Visibility Measurement
Engagement	Fixed diagnostic	Standing monthly work
Term	One time, about one week	Three-month initial term, then month to month
Scope is set by	The approved question set, validated provider surfaces, locations, and competitors included in the baseline.	The approved question set, validated provider surfaces, locations, competitors, and remediation backlog.
Cadence	One measurement window	After live validation, sampling is planned weekly across configured ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode surfaces, with a written monthly report and review call.
What you get	A planned live measurement window, first written report, and prioritized fix plan. Live measurement starts only after provider sampling is recorded.	After provider sampling is recorded, you can see how often the sampled surfaces name you, how often a mention carries a link, and which sources are cited, with the sample size and uncertainty range beside every rate.

The operator

Who you're working with.

Jeremy Cleland is the sole operator. About gives the background and its limits; Selected Work links the public technical evidence, contribution, status, and limitations.

About Jeremy Inspect selected work

What this engagement does not include.

Guaranteed placement. No one can promise a model will name you. After live validation, the engagement commits to a versioned method and comparable windows. Intervals remain conditional on the sampling assumptions, classification remains heuristic, and before-and-after movement does not by itself prove cause.

Keyword rank tracking. Different channel, different measurement, and there are competent vendors who do only that. Some underlying work — profile accuracy and structured data — supports both channels, so the scope identifies overlap instead of presenting the same fix twice.

Content production at volume. This is not a blog-post subscription. Where a specific gap points at missing content, you get the brief and the reason; writing it at scale is a separate arrangement.

A dashboard login. The deliverable is a written report from someone who read the samples, plus the remediation work. A dashboard without interpretation shifts the analysis back to the client.

Questions

Asked and answered.

What does the AI Visibility Baseline include?: The planned baseline builds the buyer-question set with you, records approval, samples a complete window across the validated provider surfaces, and delivers a first report with a prioritized fix plan. It will not be sold or described as live measurement until a credentialed provider run is recorded.
How is this different from a free AI visibility checker?: The free scanner checks page readiness; it does not query answer engines at all. The planned engagement repeats an approved question set across validated ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode provider surfaces, records the provider/model identifier, and reports each rate with its sample count and uncertainty range. Provider API output may differ from the consumer interface, and the report says so.
Can you guarantee ChatGPT will recommend my business?: No. After provider sampling is recorded, the engagement commits to a versioned method and comparable windows. Uncertainty ranges are descriptive under the sampling assumptions; repeated model outputs may not be independent, recommendation classification is heuristic, and before-and-after movement is not causal proof. Offline test output is never used as measurement.
How are the monthly scope and fee set?: After the baseline, the written scope accounts for the approved questions, answer engines, locations, competitors, and the remediation backlog. The engagement starts with a three-month term and then moves month to month. You receive the scope, cadence, decision boundaries, and fee together before deciding.
Is this just local SEO with a new name?: No. Local SEO measures visibility on search and map results; this measures whether sampled answer engines name and cite the business. The work can overlap around profiles, structured data, and citations, so that overlap is identified in the scope rather than presented as two separate fixes.
When should I expect the numbers to move?: There is no defensible fixed timetable. The first window is a baseline, not a result. Later reports show whether a rate changed beyond its uncertainty and which sources changed with it. They do not attribute calls or revenue to AI visibility without evidence that can support that claim.

Find out where you actually stand.

The free scan checks any URL against the published technical-readiness rubric. It shows the page evidence behind the score and labels the answer-engine behavior a single fetch cannot measure.

Discuss the AI Visibility Baseline Run the AI Visibility Technical-Readiness Scan