Coastal Comfort Heating & Cooling
This sample shows how the audit reads a real local business. Six sections that determine whether AI platforms can find, verify, and cite you when a customer asks for a recommendation. The score reflects how the business shows up today. The priorities are the order we would address them.
What is suppressing visibility now
- 1No structured data on the website. The site emits no LocalBusiness, Service, or FAQ schema at all, so the engines that lean on extractable markup have nothing clean to read. This is the single biggest reason ChatGPT and Perplexity describe the business vaguely or skip it entirely, and it shows up as a deduction in three sections.
- 2NAP fragmentation across the listings. The website footer carries a 910 mobile number while the Google Business Profile uses a different 910 office line, and the Yelp listing still shows the pre-move Leland address. The engines cannot resolve one clean entity, so corroboration is weak and Perplexity will not rank the business.
- 3Paid search is leaking on intent. Broad match is paying for renters, DIY repair queries, and out-of-area clicks that will never convert for an installation business. The campaign is structured and tracked, but the match types and missing negatives are spending money the audit would redirect.
What is working and worth protecting
- 1The review profile is genuinely strong. 142 Google reviews at a 4.8 average, recent and well distributed, with owner responses. This is why the business already wins the Google local pack and appears in Google AI Overviews for core service queries.
- 2The content is better than the structure. The blog answers real homeowner questions about heat pumps, humidity on the coast, and system sizing, and it reads with genuine expertise. The substance is there. The markup that would let AI lift it cleanly is not.
- 3Google already cites the business. On the geographic and category queries that trigger an AI Overview, Coastal Comfort is named with the site cited. That proves the entity is real and trusted enough to surface. The job is to extend that to the engines that need structure.
The priority order.
Add structured data to the website.
LocalBusiness, Service, and FAQ schema, all with one consistent name, phone, and address. This is the highest leverage move in the audit and the lever that converts ChatGPT and Perplexity from vague to specific.
Unify NAP to one canonical phone and one address everywhere.
Pick one number and the current address, then make the website, the Google Business Profile, Yelp, and every directory agree. This is the corroboration fix that lets the review weighted engines start counting the reviews.
Cut the wasted paid search spend with negatives and tighter match types.
Add the renter, DIY, and out-of-area terms as negatives, move the leakiest broad match keywords to phrase or exact, and confirm geo targeting holds to the two counties. Fastest dollar for dollar win in the account.
On the review call, we walk through the findings together, agree on the sequence, and scope the Partnership tier that fits: the schema and NAP fixes get owned, the prompt set keeps running, and the movement gets read month over month.
30 named criteria. Six sections. 100 points.
Each section is rated against five named criteria, each Met, Partial, or Not Met based on specific evidence. The criteria roll up to a section score, and the sections to an overall score out of 100.
AI Citation Presence
- ChatGPT citation
- Perplexity citation
- Gemini citation
- Google AI Overviews presence
- Google AI Mode citation
Google Business Profile
- Category and service coverage
- Profile completeness and freshness
- Review profile strength
- Active profile engagement
- NAP consistency with website
Website Architecture
- Schema markup coverage
- Semantic page structure
- Service page depth
- AI crawler accessibility
- Technical foundation
Content Depth and Authority
- Cornerstone content
- FAQ content with schema
- Extractable specificity
- Semantic structure
- Content freshness
Local Search Footprint
- Local pack visibility
- Organic rankings for core queries
- Citation coverage and consistency
- Cross-source fact corroboration
- UGC and third-party mentions
Advertising Baseline
- Keyword intent alignment
- Landing page quality
- Conversion tracking
- Campaign structure
- Attribution clarity
AI Citation Presence
We ran the queries a homeowner would actually type into ChatGPT, Perplexity, Gemini, Google AI Overviews, and Google AI Mode when looking for a business like this one. We documented whether the business appeared in the answer, what content was cited, how strong the citation was, and who was cited if the business was not.
Names the business occasionally on direct branded queries, but on unbranded category and geo queries it leads with competitors and describes Coastal Comfort in generic terms when it appears at all. The cause is structural. There is no schema and little extractable specificity for the model to lift.
Does not surface the business in best-company answers and does not cite the site. Perplexity leans on public review volume and corroborated facts, and the split listings and missing structure cost it here.
Mentions the business inconsistently, sometimes with the wrong service area attached. It recognizes the name but is unsure where the business operates, a direct symptom of the NAP fragmentation.
The strongest surface. On category and geo queries that trigger an Overview, Coastal Comfort is named and the site is cited, carried by the strong Google Business Profile and reviews.
Names the business for branded and some geo queries with the site cited, but pulls competitors first on the broader unbranded queries where structure and corroboration decide the order.
The business is trusted by Google, which sees the reviews and the local pack position, and discounted by the engines that need clean structure and corroboration to cite confidently. The recommendation path is structure and consolidation, not more content. The content is already good. The engines just cannot read it cleanly or agree on who and where this business is.
In a live audit, this section includes captioned screenshots of each platform answer, run on a dated day, logged out, with explicit geographies. This sample omits the images, since the business is illustrative.
What the tracking software actually recorded.
Alongside the manual prompt runs, we set up a tracked prompt set in AI visibility software: 12 prompts a real customer would type, built across the six decision stages a buyer moves through, monitored for seven days across ChatGPT, Perplexity, and Google AI Overviews. This is the machine-read baseline, not an estimate. Reporting by stage is what turns one number into a diagnosis.
Twelve tracked prompts, two per stage. Each prompt carries equal weight, so the stage rates average to the 20.7% above. The stage a business wins tells you more than the average across all of them.
The number that matters is not 20.7%. It is that nearly two thirds of it comes from the two branded prompts, where someone already knows the name, while Discovery, Comparison, and Urgency sit at zero. The business is findable to people who already have it in mind and invisible to everyone else. The pages being cited instead are a competitor’s service page, a directory listing, and a Reddit thread, and only one page the business owns appears in the top ten at all. That gap is what the rest of this analysis is about.
Give the engines something clean to read
The same fix Section 03 calls for. Add LocalBusiness, Service, and FAQ schema so ChatGPT and Perplexity can extract who you are, what you do, and where, instead of guessing. This is the lever that moves the structure dependent surfaces.
Resolve where the business operates
Gemini is unsure of the service area because the listings disagree. One canonical address and service area, repeated across the site and every directory, tells the engines exactly where to place you.
Hold the Google lead
Google AI Overviews already cites the business. Protect it by keeping the profile complete, the reviews flowing, and the service pages current as the schema work ships.
Re-run this baseline every 90 days
Re-run the same 12 prompts quarterly, logged out with explicit geos, holding the stage allocation steady so the comparison holds up. The number to watch is not the overall score. It is whether Discovery and Comparison move off zero.
Google Business Profile
The Google Business Profile is the single highest leverage local visibility asset a business has. AI platforms do not use it to rank you. They use it to verify you. We reviewed categories, services, reviews, photos, posts, and NAP consistency against what AI needs to confidently cite the business.
Primary category is set to HVAC contractor with relevant secondaries. Specific and well matched to the work. No change needed.
Hours, service area, and services are filled, the description is specific, and recent photos are present. A complete, credible profile.
142 Google reviews at 4.8 stars, recent and well distributed, with owner responses on most. This is the asset carrying the business in the Google surfaces. Genuinely strong.
Google Posts ran regularly last year but have gone quiet in recent months. The lever is consistency. A profile that keeps posting signals an active business to the surfaces that read it.
The gap. The profile lists one 910 office number while the website footer shows a different 910 mobile line, and the address on Yelp predates the move from Leland. The profile itself is clean, but it does not match the website or the directories, so the engines cannot resolve one entity.
Unify NAP to one canonical phone and address everywhere
Pick one number and the current address. Make the website, the Google Business Profile, Yelp, and every directory match exactly. This is the corroboration fix that lets the review weighted engines start counting the reviews this business has earned.
Correct the outdated Yelp address
The Yelp listing still shows the pre-move location. Update or reclaim it so it carries the current address and the canonical phone.
Restart a steady posting cadence
Resume Google Posts on a regular rhythm. Recent jobs, seasonal service reminders, and offers all signal an active business to the surfaces that read the profile.
Website Architecture for AI Retrievability
AI platforms read a website the way a researcher reads a source, looking for verifiable facts, clear service descriptions, named expertise, and location context. We checked five things that decide whether AI platforms can read, understand, and trust the site: schema markup, semantic structure, service page depth, crawler accessibility, and technical foundation.
None. The site emits no LocalBusiness, Service, or FAQ structured data. To an AI engine, the business has not declared who it is, what it does, or where, in the machine readable form the engines lean on. This is the largest single deduction in the audit.
Mixed. Most pages have one clear H1 and reasonable headings, but a few service pages stack multiple H1s and bury the service name below decorative copy.
Uneven. The main HVAC pages are solid, but several service area pages are thin and near duplicates of each other, which reads as low value to the engines.
Clean. robots.txt does not block the major AI crawlers, and the sitemap is present and valid. GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and OAI-SearchBot are all allowed.
Sound. HTTPS, a mobile viewport, and analytics are all in place, and core pages load acceptably.
The site is built on Wix. Wix supports page-level structured data up to a character limit per page, and anything beyond that has to be placed as custom code in the page head, which is workable but easy to break in routine edits. The schema recommended in this audit fits within what the platform supports today. If the schema footprint outgrows those limits later, the options are managing the custom code carefully or moving to a platform that carries it natively.
Add LocalBusiness, Service, and FAQ schema
The highest leverage fix in the audit. Declare one canonical business entity with a single phone and address, mark up the core services, and add FAQ schema to the pages that answer real homeowner questions. Wix’s page-level structured data fields carry this within their per-page limit; anything larger goes in the page head as custom code.
Clean up the heading structure
One H1 per page, naming the service directly and near the top. This is a quick fix that helps both the engines and human readers parse each page.
Deepen or consolidate the thin service area pages
The near duplicate area pages either get real, locally specific content or get consolidated. Thin, repetitive pages dilute the entity rather than strengthen it.
Content Depth and Authority
AI platforms preferentially cite businesses that have shown topical expertise through depth, specificity, and well structured content. Volume alone does not earn authority. We looked at whether the content is cornerstone worthy, specific enough to extract, and structured so AI can lift usable passages from it.
Strong. The blog carries genuinely useful guides on heat pump sizing, coastal humidity, and when to repair versus replace, written with real expertise. This is the foundation other businesses in the category lack.
Missing. The site answers common questions inside paragraphs, but there is no dedicated FAQ content and no FAQPage schema. FAQ with schema is the most AI extractable format for a local business, and it is the clearest open opportunity here.
Good. The content names specific systems, timelines, and coastal conditions rather than speaking in generalities.
The blog posts are chunked and scannable, with clear subheads and short paragraphs.
Recent. Posts are dated into this year and the topics track the season.
Add FAQ blocks with schema to the top service pages
Take the questions the blog already answers and add short, genuine FAQ blocks with FAQPage schema to the highest intent service pages. This converts content the business already has into the format AI extracts most readily, and it ties directly to the Section 03 schema work.
Keep the cornerstone cadence and add author signals
The content engine is strong. Maintain the cadence and add clear authorship and visible dates so the expertise is attributed and source worthy, not anonymous.
Local Search Footprint
This section tests both baseline organic visibility and the AI era question of whether key business claims are corroborated across multiple sources. AI platforms trust facts that appear in more than one place. Claims that only live on the website are discounted. We evaluated local pack visibility, organic rankings, citation coverage, cross source corroboration, and third party mentions.
Strong. The business appears in the Google local pack for core HVAC queries in Wilmington and the nearer Brunswick County towns, carried by the review profile.
Solid. The main service pages rank on the first page for the primary repair and installation queries in the core service area.
The gap. Coverage exists across the major directories, but the name, address, and phone disagree from one listing to the next. Inconsistent citations are worse than missing ones, because they actively split the entity.
Partly there. The specialty and reviews corroborate across sources, but the address and phone do not, which weakens the overall picture.
Present on the review platforms and a community group or two, thin on press and beyond. Room to grow earned mentions.
AI platforms verify claims across sources. A claim that only appears on the website is treated as unverified.
Unify NAP across every listing
Ties to Section 02. One canonical name, address, and phone across the website and every directory. This is the highest leverage local fix and the one the corroboration test is failing on.
Confirm presence on Bing Places and Apple Maps
Confirm the business is listed with consistent NAP on Bing Places and Apple Maps. These feed sources the engines check and are often missing for local businesses.
Grow earned mentions
Pursue local press and community mentions beyond the directories. Third party mentions strengthen the corroboration the engines reward.
Every section that touches the contact identity points back to the same root cause. One canonical name, phone, and address, repeated everywhere, resolves the corroboration gap here, the NAP gap in Section 02, and a large share of the citation gap in Section 01.
Advertising Baseline
This section applies when paid advertising is already part of the marketing. It is not a full campaign audit. It is a structural read to catch obvious misalignments that would undercut every other recommendation in this analysis, like paying for awareness traffic when you need buyer intent traffic.
Section 06 uses binary scoring. Each criterion is either Met (1 point) or Not Met (0 points).
Off. Broad match is paying for renter queries, DIY repair searches, and clicks outside the two county service area. The seed keywords are right. The match types and missing negatives are not.
Good. Paid traffic lands on a dedicated service page rather than the home page, which is the right call.
Recording. Calls and form fills are tracked, so the data to optimize on exists.
Loose. One catch all campaign mixes repair, installation, and maintenance with no segmentation, which makes the wasted spend hard to isolate.
Reasonable. Phone and form conversions are attributed cleanly enough to see what is working once the structure is tightened.
Cut the wasted spend with negatives and tighter match types
Add the renter, DIY, and out-of-area terms as negatives, move the leakiest broad match keywords to phrase or exact, and confirm geo targeting holds to New Hanover and Brunswick. The fastest dollar for dollar win in the account.
Segment the catch all campaign
Split repair, installation, and maintenance so spend and intent can be read and managed separately. Segmentation is what turns a tracked account into an optimizable one.
What we would do next.
Based on what the audit surfaced, there are four paths forward. One is the right next step for this business right now. We recommend that one, and explain below why the others are not it.
Stay the course
No engagement needed. For some businesses this means the gaps are addressable with the existing team and the action plan provided. For others the foundation is already strong and the work is to maintain it. Either way, this is the right path when bringing in a strategic partner is not what the business needs right now.
Launch and Learn
A 90-day sprint to test whether paid media is the right lever. A focused campaign with a dedicated landing page and clear performance markers at the 30, 60, and 90-day points, so the business learns from real spend what converts before committing to anything ongoing.
AI Visibility Partnership
The gaps are specific to the answer layer and the business already has people handling the rest. This path puts ongoing ownership of entity architecture, schema, citation surfaces, and answer-shaped content in one place, with monthly measurement against the prompts real buyers type. How much we execute versus hand to your team is decided in the scope.
Fractional CMO engagement
The gaps are systemic and the business is positioned for sustained growth that justifies ongoing strategic marketing leadership. This path integrates AI visibility, content, paid media, and CRM into one coherent direction over time.
The gaps here are bounded and they share one root cause. Add structure, unify the entity, tighten the paid media, and the surfaces that already trust this business on Google start citing it everywhere else. Path A would let the highest leverage fixes wait, and they need hands. Path C is the right shape but the wrong moment: an ongoing partnership makes sense once we know which fixes moved the needle, and this business has not run that test yet. Path D overshoots, because this business is healthy, not systemic. Launch and Learn ships the bounded fixes inside 90 days and proves the lift, with the Partnership or Fractional CMO a natural graduation once the foundation is in place.
The review call. The 30 minute review call is included. We walk through the findings together, answer your questions, and pressure test the recommendation. You leave with either everything you need to act on your own, or a clear picture of what working together would look like.
Your decision. The recommendation is a read on what fits your situation. The decision is yours. If the path is something to run internally or with a trusted partner, the analysis is built to support that. If you want us involved, you get a scoped proposal within 48 hours of the call.
From here. The priority order in the analysis is the order to work it, whether you are filling gaps or protecting a strength. AI visibility is not a one time fix. Platforms shift, competitors invest, and what works today can need attention next quarter.
This analysis is a sample.
Want this read on your own business?
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