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Search is no longer limited to a list of blue links.
A potential customer looking for a plumber in Toronto can now ask an AI platform a question such as “Who are the best plumbers in Toronto?”, “What is the best emergency plumber in Toronto 24/7?”, or “Who offers affordable plumbing services in Toronto with upfront pricing?” Instead of returning a conventional results page, generative search can provide a synthesized answer, a shortlist of businesses, comparisons, recommendations, and supporting information.
For local service businesses, this changes the definition of search visibility.
Ranking on Google remains important, but businesses increasingly need to consider another question:
When a potential customer asks an AI system which business they should choose, does that business appear in the answer?
That was the challenge addressed by ThatWare for Everest Drain & Plumbing, a local residential and commercial plumbing business serving Toronto and the Greater Toronto Area (GTA).
At the beginning of the campaign, Everest Drain & Plumbing was experiencing low organic visibility, limited clicks and impressions, and insufficient visibility for important plumbing-related searches. Several core service, emergency, location-specific, commercial, and problem-based queries were not achieving the desired first-page visibility. The challenge was therefore broader than simply improving a handful of conventional keyword rankings.
ThatWare approached the challenge through an integrated combination of traditional SEO, Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), local SEO, content optimization, structured data, internal linking, geo-targeted content, and authority building.
A central component of the campaign was understanding the 3 C’s of search intent: Content Type, Content Format, and Content Angle. Rather than producing content simply because a keyword had search volume, the strategy considered what a user actually wanted when asking a particular question and what type of content could best satisfy that intent.
The campaign also extended beyond conventional search engines by testing the brand’s visibility across ChatGPT, Claude, and Gemini. The observed results included Everest Drain & Plumbing appearing at position #2 on ChatGPT for queries such as “Top 10 Plumber in Toronto” and “Who are the best plumbers in Toronto right now?” It appeared at position #4 on Claude for “Best emergency plumber in Toronto 24/7” and “Best company for basement flood prevention in Toronto.” On Gemini, Everest appeared at position #1 for “Trusted local plumbers near downtown Toronto” and “Affordable plumbing services in Toronto with upfront pricing.”
These were observed results from the campaign’s AI-search testing, rather than universal or permanent rankings. Generative results can change depending on the query, context, platform, model, location, and time.
The significance, however, is clear.
Everest Drain & Plumbing moved from a situation of limited search visibility toward measurable inclusion in AI-generated recommendations for highly relevant local and commercial queries.
This case study explains how ThatWare approached that transformation, why search intent became the foundation of the strategy, how the SEO/AEO/GEO layers worked together, and what the results reveal about the future of local search.
The New Search Landscape: Why Traditional SEO Alone Is No Longer Enough
For years, search engine optimization was largely organized around a familiar objective: identify keywords, optimize pages, build authority, improve rankings, and increase organic traffic.
That model remains valuable.
But the search experience is changing.
Users can now interact with search technology in increasingly conversational ways. Instead of entering a short phrase such as “plumber Toronto,” a user can formulate a much more specific question:
“Who are the best emergency plumbers in Toronto that offer 24/7 service?”
That distinction matters because the second query contains considerably more information about the user’s intent.
It communicates:
- The service required.
- The geographic area.
- The urgency.
- The desired availability.
- The user’s expectation of a recommendation.
The evolution of search has moved through multiple stages—from traditional search results to featured snippets, People Also Ask results, knowledge panels, AI-powered search experiences, and increasingly conversational generative interfaces. The strategic implication is that businesses must think beyond individual keywords and understand the questions and decisions behind those searches.
From Ten Blue Links to Answer Engines
Traditional search engines generally required users to review multiple results before deciding which page might contain the answer they needed. Advanced SEO now goes beyond rankings by optimizing content, entities, user intent, technical foundations, and structured information to improve visibility across modern search and AI-powered answer engines.
Answer-oriented experiences changed that behavior.
A searcher might receive a direct answer through a featured snippet. They might expand a People Also Ask result. They might see a knowledge panel that summarizes an entity. More recently, generative interfaces can synthesize information into an answer designed to satisfy the query directly.
This creates a new layer of competition.
Businesses are no longer competing only to be clicked from a results page. They may also be competing to become one of the sources, entities, or recommendations considered by an AI system when it constructs an answer.
For local service businesses, that distinction is particularly important.
A customer searching for a plumbing company is rarely interested in information for its own sake. They may be trying to solve an immediate problem, compare providers, understand pricing, find an emergency service, or determine which local company appears trustworthy.
Generative search can therefore function as a recommendation layer.
What Is Generative Search?
Generative search refers broadly to search experiences in which an AI system interprets a user’s query and generates a synthesized response rather than simply displaying a conventional list of links.
Depending on the platform and query, the response may include:
- Direct answers.
- Business recommendations.
- Comparisons.
- Summaries.
- Lists.
- Local service suggestions.
The user does not necessarily need to visit multiple pages to understand the options. The AI interface may perform part of that discovery and comparison process for them.
For a business, this means being mentioned or recommended can become part of the customer acquisition journey.
What Is Answer Engine Optimization?
Answer Engine Optimization (AEO) focuses on structuring and developing content so that it can effectively answer specific user questions.
The objective is not simply to insert a keyword into a paragraph.
Effective AEO considers whether content is:
- Directly answerable.
- Clearly structured.
- Contextually relevant.
- Easy for systems to interpret.
- Aligned with the question being asked.
This naturally encourages a different approach to content creation.
Instead of asking only:
“What keyword should this page rank for?”
an AEO-focused strategy also asks:
“What question is the user asking, and can this page provide the clearest, most useful answer?”
The ThatWare campaign applied this principle by incorporating question-based content, concise answer sections, FAQs, structured headings, semantic relationships, and conversational queries into the broader optimization process.
What Is Generative Engine Optimization?
Generative Engine Optimization (GEO) extends this thinking into AI-generated search experiences.
GEO focuses on improving the signals and content environment that help a business become relevant to generative systems.
Key considerations include:
- Entity relevance.
- Brand relevance.
- Semantic relationships.
- Topical authority.
- Structured information.
- Citations and mentions.
- Brand and entity consistency.
In other words, GEO is not simply “SEO for ChatGPT.”
It requires thinking about how a business is represented as an entity, how its services relate to specific locations and customer problems, how consistently those relationships appear across its digital ecosystem, and whether the business has sufficient topical and contextual relevance to be considered for particular queries.
Why Local Businesses Are Particularly Affected
Local service businesses are especially relevant to generative search because their customers frequently make recommendation-oriented searches.
A user may ask an AI platform to identify a provider based on:
- Location.
- Service type.
- Emergency availability.
- Trust.
- Pricing.
- Experience.
- Reviews.
- A specific problem.
- Residential or commercial requirements.
Plumbing is an excellent example.
Consider the difference between:
“plumber Toronto”
and:
“Who are trusted local plumbers near downtown Toronto?”
The first describes a category.
The second describes a decision.
That distinction became central to the Everest campaign.
About Everest Drain & Plumbing: The Business Behind the Case Study
Everest Drain & Plumbing operates within the local plumbing services market, serving residential and commercial customers in Toronto and the Greater Toronto Area.
The business context is important because plumbing is inherently connected to location and urgency.
A plumbing customer may need:
- Emergency plumbing.
- Drain services.
- General plumbing services.
- Location-specific plumbing support.
- Preventive solutions.
- Residential services.
- Commercial services.
The campaign therefore needed to establish relevance across both broad service categories and specific customer needs. The case-study strategy specifically addressed emergency plumbing, drain services, basement flood prevention, affordability, local trust, and Toronto-focused service intent.
The Search Environment
The Toronto plumbing market presents a highly competitive local search environment.
Customers may search using short, conventional terms such as:
- Plumber Toronto.
- Emergency plumber Toronto.
- Drain cleaning Toronto.
- Plumbing company Toronto.
But their actual decision-making questions can be significantly more detailed:
- Who are the best plumbers in Toronto?
- Which company provides emergency plumbing 24/7?
- Who can help prevent basement flooding?
- Which local plumbing company is trustworthy?
- Who provides affordable plumbing with upfront pricing?
- Which plumber serves downtown Toronto?
These queries contain commercial, local, and problem-specific signals.
They also provide an AI system with much more context about what the user is trying to accomplish.
Why AI Visibility Matters for a Plumbing Business
There is an important difference between:
Someone searches for plumbers.
and:
Someone asks AI which plumber they should choose.
In the first scenario, the user is presented with a set of results and must evaluate them.
In the second, the AI system may provide a shortlist or recommendation.
That means the search experience itself can influence consideration.
For Everest Drain & Plumbing, the opportunity was not to abandon conventional SEO. It was to expand the definition of search visibility so that traditional organic presence and generative recommendation visibility could work together.
The Initial Challenge: Low Visibility for High-Intent Plumbing Searches
Before implementing the strategy, Everest Drain & Plumbing was experiencing very low organic visibility, with limited clicks and impressions for important plumbing-related searches.
The presentation identifies the central problem clearly: many core service pages were not ranking on the first page for competitive local searches.
That created several challenges.
Limited Organic Clicks and Impressions
Visibility is the first stage of the search journey.
If a business does not appear prominently for relevant queries, potential customers may never reach its website.
In Everest’s case, limited organic visibility meant that important service and location searches were not generating the desired level of exposure.
The challenge was therefore not simply about increasing traffic to existing pages. It was about improving the site’s overall relevance to the search ecosystem.
Important Queries Were Not Generating Strong First-Page Visibility
The campaign needed to address multiple query categories:
Core service queries
These describe the primary services the business offers.
Emergency queries
These represent urgent users who may have an immediate plumbing problem.
Location-specific queries
These connect plumbing services with Toronto, GTA, and more geographically specific searches.
Commercial-intent queries
These indicate that a user may be evaluating a provider.
Problem-specific searches
These describe a plumbing issue or preventative need, such as basement flood prevention.
This mix made it clear that one generic service page would not be enough.
Different questions required different content types, formats, and angles.
The Gap Between Business Expertise and Search Visibility
A local business can have strong operational expertise and still have insufficient digital visibility.
That is one of the most important principles behind this case study.
A plumbing company may know how to solve a customer’s problem. But search systems need to understand:
- Who the business is.
- What it offers.
- Where it operates.
- Which services it specializes in.
- Which customer problems it solves.
- Why it is relevant to a particular query.
The problem was therefore not simply:
“Create more content.”
It was:
“Create the right digital signals around the questions and services that matter.”
The Generative Search Challenge
Generative search introduced another layer.
A traditional search engine may evaluate a page for relevance to a query.
An AI system generating a recommendation may need to understand the broader context surrounding the business.
For example:
Everest Drain & Plumbing
needs to be contextually associated with:
Plumbing → Toronto → GTA → Emergency Plumbing → Drain Services → Residential → Commercial → Flood Prevention
The campaign therefore focused on creating and strengthening these relationships throughout the site’s content ecosystem.
Defining the Searcher’s Intent Before Creating Content
One of the most important elements of the campaign was the 3 C’s of Search Intent:
- Content Type
- Content Format
- Content Angle
This framework helped ThatWare move beyond keyword-centric content planning.
A keyword is only the surface representation of a search.
Intent explains why the user searched.
1. Content Type
Different searches require different types of content.
A user looking for an emergency plumber does not necessarily need a 2,000-word educational article before taking action.
A user researching basement flood prevention may need an in-depth guide.
A user comparing service providers may need a commercial landing page or comparison-oriented content.
Relevant content types can include:
- Service pages.
- Location pages.
- Blog posts.
- Comparison content.
- FAQs.
- Guides.
- Commercial landing pages.
The objective is to match the asset to the intent.
2. Content Format
Once the content type is determined, the format also matters.
Different questions may be best answered using:
- Lists.
- Step-by-step guides.
- FAQs.
- Comparisons.
- Definitions.
- Service explanations.
- Local recommendations.
- Problem/solution content.
For example, a user asking:
“What causes basement flooding?”
may benefit from an explanatory guide.
A user asking:
“Who are the best plumbers in Toronto?”
may expect a list or recommendation-oriented answer.
A user asking:
“What plumbing services are available in Toronto?”
may benefit from a structured service overview.
The content format should therefore follow the searcher’s need.
3. Content Angle
The third C is the content angle.
The same general topic can have very different search intents.
Compare:
“Plumbing services Toronto”
with:
“Affordable plumbing services in Toronto with upfront pricing.”
and:
“Best emergency plumber in Toronto 24/7.”
All three relate to plumbing services.
But they represent different motivations.
The first is broad local discovery.
The second emphasizes affordability and pricing transparency.
The third emphasizes urgency and availability.
A successful AEO/GEO campaign needs to recognize these differences rather than treating them as variations of one keyword.
Mapping Search Intent to AI Questions
| User Intent | Example Query | Appropriate Content Direction |
| Local discovery | Best plumbers in Toronto | Local authority content |
| Emergency | Best emergency plumber in Toronto 24/7 | Emergency service content |
| Commercial | Affordable plumbing services | Commercial landing page |
| Problem solving | Basement flood prevention | Informational guide |
| Service-specific | Drain cleaning Toronto | Dedicated service content |
| Trust | Trusted local plumbers | Authority and trust content |
This intent mapping became the foundation for the rest of the campaign.
ThatWare’s Generative Search Strategy for Everest Drain & Plumbing
ThatWare did not treat AEO or GEO as replacements for SEO.
Instead, the campaign used a multi-layer search strategy.
The framework combined:
Traditional SEO + Local SEO + AEO + GEO + Content + Technical SEO + Authority
Each layer addressed a different part of the search journey.
Layer 1 — Traditional SEO Foundation
Traditional SEO remained essential.
The campaign addressed:
- Keyword research.
- Search intent.
- On-page optimization.
- Technical foundations.
- Internal linking.
- Content architecture.
Why?
Because generative search optimization still depends on having a strong digital information environment.
If a website is difficult to crawl, poorly structured, inconsistent, or lacking relevant content, simply adding AI-focused language does not solve the underlying problem.
Traditional SEO provides the foundation on which AEO and GEO can operate.
Layer 2 — Answer Engine Optimization
The next layer focused on answering questions.
That included:
- Direct answers.
- Question-based content.
- FAQs.
- Concise answer blocks.
- Structured headings.
- Semantic relationships.
- Conversational queries.
The goal was to make important information easier to identify and understand.
Instead of hiding the answer deep within a page, content could directly address the question being asked.
Layer 3 — Generative Engine Optimization
The GEO layer focused on strengthening:
- Entity clarity.
- Brand relevance.
- Service-location relationships.
- Topic authority.
- Contextual content.
- Supporting pages.
- Structured data.
This helped shift the campaign from simply optimizing pages toward building a stronger information ecosystem around the business.
Layer 4 — Local Search Optimization
Because Everest serves a defined geographic market, local relevance was critical.
The strategy incorporated:
- Toronto.
- GTA.
- Location relevance.
- Local service intent.
- Emergency intent.
- Residential intent.
- Commercial intent.
A generic plumbing page is less useful than a page that clearly establishes the relationship between a service, the business, and the geographic market.
Layer 5 — AI Platform Visibility
The final layer involved testing visibility across:
- ChatGPT.
- Claude.
- Gemini.
This cross-platform approach mattered because different AI systems can produce different recommendations for similar prompts.
The goal was therefore not to optimize for one isolated response.
It was to establish a stronger overall digital presence capable of supporting visibility across multiple generative search environments.
Step 1 — Conducting Keyword Research for AI Search
The campaign specifically included keyword research for AI tools.
This required expanding the traditional concept of keyword research.
Traditional Keywords vs Conversational Queries
Traditional SEO might begin with:
- Plumber Toronto.
- Emergency plumber Toronto.
- Drain cleaning Toronto.
These remain useful.
But generative search introduces longer, more conversational forms:
- Who are the best plumbers in Toronto?
- What is the best emergency plumber in Toronto 24/7?
- Who provides affordable plumbing services in Toronto?
- Who are trusted local plumbers near downtown Toronto?
These queries reveal more about the user’s decision-making process.
Identifying High-Intent Queries
The campaign focused on intent signals such as:
Purchase intent
The user is actively evaluating providers.
Urgency
The user has an immediate plumbing problem.
Location
The user needs a provider in a specific geographic area.
Service specificity
The user is looking for a particular type of plumbing service.
Trust
The user wants a provider they can rely on.
Price sensitivity
The user wants affordability or pricing transparency.
These signals helped identify queries that could have stronger commercial significance than generic informational terms.
Building an AI Search Query Set
Rather than tracking only conventional keyword variations, the strategy could organize AI queries into several categories:
| Query Category | Example |
| Informational | How can I prevent basement flooding? |
| Recommendation | Who are the best plumbers in Toronto? |
| Comparison | What should I look for in a Toronto plumbing company? |
| Local discovery | Who are trusted plumbers near downtown Toronto? |
| Emergency | Who is the best 24/7 emergency plumber in Toronto? |
| Pricing | Who offers affordable plumbing with upfront pricing? |
| Problem-based | Who specializes in basement flood prevention? |
This creates a more realistic representation of how customers actually interact with conversational search.
Step 2 — Creating SEO-Optimized Blog Content
The campaign also included the creation of SEO-optimized blog content.
But the objective was not to produce content merely to increase the number of indexed pages.
The content needed to build topical coverage.
Building Topical Coverage
The campaign addressed subjects surrounding:
- Plumbing.
- Drain services.
- Emergency plumbing.
- Flood prevention.
- Local plumbing problems.
- Maintenance.
- Pricing.
- Residential plumbing.
- Commercial plumbing.
This created a broader content environment around the business.
For example, a page about emergency plumbing can be supported by content discussing:
- Common plumbing emergencies.
- What to do before a plumber arrives.
- Emergency response considerations.
- When a plumbing issue requires immediate attention.
Likewise, flood-prevention content can be supported by information about:
- Basement water risks.
- Drainage issues.
- Preventive plumbing measures.
- Signs of potential problems.
The purpose is to demonstrate relevance across a topic rather than relying on one page.
Creating Content That Answers Questions Directly
AEO-friendly content benefits from clear structure.
That means using:
- Descriptive H2 and H3 headings.
- Short answer sections.
- Definitions.
- Lists.
- Tables.
- FAQs.
- Practical recommendations.
This structure helps users scan the content while also making relationships between questions and answers clearer.
Supporting Commercial Pages
Informational content should not exist in isolation.
Relevant blog articles can connect to commercial service pages through contextual internal links.
For example:
Emergency Plumbing Guide → Emergency Plumbing Service Page
This gives the user a logical path from information to action.
Step 3 — Building AI-Friendly Landing Pages With Structured Data
Another implementation step was the creation of AI-tool-focused landing pages with structured data.
The important principle here is that structured data supports machine understanding; it should not be treated as a magic mechanism that automatically produces ChatGPT, Claude, or Gemini rankings.
Why Dedicated Landing Pages Matter
A dedicated page can establish a clear relationship between:
- The business.
- A specific service.
- A geographic market.
- A customer need.
- Commercial intent.
For example, an emergency plumbing page can clearly communicate that the business provides emergency plumbing services in the relevant service area.
That is much more useful than expecting one generic homepage to communicate every possible service and intent.
Structured Data and Machine Understanding
Relevant structured-data concepts can include:
- LocalBusiness.
- Service.
- Organization.
- Breadcrumb.
- FAQPage, where appropriate.
- Relevant business information.
The objective is to provide clearer machine-readable information about what the page and business represent.
Structured data should complement visible page content rather than substitute for it.
Creating Clear Entity Relationships
A useful way to conceptualize the campaign is:
Everest Drain & Plumbing → Plumbing Services → Toronto → Emergency Plumbing → Drain Services
These relationships establish connections between:
- Business.
- Service.
- Location.
- User problem.
The clearer those relationships become across the site, the easier it is to understand the overall subject of the business.
Step 4 — Implementing a Strategic Internal Linking Architecture
Internal linking was another major component of the campaign.
A strong internal linking strategy helps turn individual pages into a connected information system.
From Isolated Pages to Topic Clusters
The campaign’s architecture can be represented as:
Pillar → Service → Location → Supporting Content
For example:
Plumbing Services
↓
Emergency Plumbing
↓
Emergency Plumbing Toronto
↓
Supporting emergency plumbing guides and FAQs
This architecture creates logical relationships between pages.
Contextual Internal Links
Internal links should make sense in context.
Good internal linking considers:
- Relevant anchor text.
- Logical page relationships.
- User journey.
- Topical relevance.
A link should help the reader move to the next useful piece of information.
Connecting Informational and Commercial Intent
One of the most useful structures is:
Informational Content → Commercial Service Page
For example:
“What to Do During a Plumbing Emergency”
can logically lead to:
“Emergency Plumbing Services in Toronto.”
The user receives useful information first and can then move toward a relevant service.
Why Internal Linking Matters for Generative Search
Internal linking contributes to a clearer topical structure.
It helps establish relationships between subjects, services, locations, and supporting content.
For an AI-oriented campaign, that broader context matters because the business should not appear as a disconnected collection of pages.
The objective is to build a coherent entity and topic ecosystem.
Step 5 — Creating Comparison Tables and Visual Content
The campaign also incorporated comparison tables and visuals.
This is particularly useful for commercial and decision-oriented searches.
Why Tables Matter
Tables can simplify complex information.
They can be used to compare:
- Service types.
- Features.
- Pricing considerations.
- Decision criteria.
- Service suitability.
For example:
| Customer Need | Relevant Service Direction |
| Urgent leak | Emergency plumbing |
| Blocked drain | Drain service |
| Flood concerns | Flood prevention |
| Business plumbing issue | Commercial plumbing |
| Routine residential issue | Residential plumbing |
A structured presentation allows users to identify the relevant option quickly.
Making Information Easier to Extract
Well-structured information typically uses:
- Clear headings.
- Consistent terminology.
- Concise descriptions.
- Logical groupings.
The objective is to remove unnecessary ambiguity.
Visual Content and Comprehension
Visual assets such as:
- Infographics.
- Diagrams.
- Service illustrations.
- Comparison graphics.
can make complex concepts easier to understand.
For a plumbing business, visual content can also explain processes and problems that are difficult to communicate through text alone.
Step 6 — Adding FAQs Based on User Search Queries
FAQs were another important part of the implementation.
The campaign focused on adding FAQs based on user search questions rather than treating FAQs as a place to repeat keywords.
Turning Search Questions Into Content Assets
Relevant questions can include:
- Who are the best plumbers in Toronto?
- How quickly can an emergency plumber respond?
- What does drain cleaning cost?
- What causes basement flooding?
- What plumbing services are available in Toronto?
- Do plumbers offer commercial services?
Each question represents a different potential search intent.
FAQ Content for AEO
A question-answer format naturally aligns with conversational search.
The structure is straightforward:
Question
→ Direct answer
→ Supporting explanation
→ Relevant service or resource
This makes the content useful to both users and answer-oriented search experiences.
FAQ Content vs Keyword Stuffing
The guiding principle should be:
Question relevance is more important than keyword repetition.
A useful FAQ answers something the customer genuinely wants to know.
A poor FAQ simply creates another opportunity to repeat the same keyword.
The difference is important because modern search optimization is increasingly about meaning and usefulness, not mechanical repetition.
Step 7 — Integrating Geo-Targeted Content for Toronto & GTA
Local relevance was fundamental to the campaign because Everest Drain & Plumbing operates in a defined geographic market.
Why Geography Matters in Local Generative Search
Local recommendations can depend on factors such as:
- City.
- Neighborhood.
- Service area.
- Proximity.
- Local relevance.
A user looking for a plumber in Toronto is not necessarily interested in a plumber located hundreds of kilometers away.
The business therefore needs to establish a strong relationship between its services and its actual geographic market.
Building Toronto-Specific Relevance
The campaign incorporated relevance around:
- Toronto.
- GTA.
- Downtown Toronto.
- Relevant service areas.
The purpose is not simply to repeat geographic names.
It is to establish meaningful local context.
Combining Location + Service + Intent
A useful framework is:
[Service] + [Location] + [Intent]
For example:
Emergency plumber + Toronto + 24/7
Drain cleaning + Toronto + residential
Plumbing services + Toronto + affordable pricing
Basement flood prevention + Toronto
Each combination represents a different opportunity.
Avoiding Low-Quality Location Scaling
Geo-targeted content must remain useful.
Creating dozens of near-identical pages with only the city name changed can produce thin content and weak user value.
A better approach is to ensure that each meaningful location page provides:
- Genuine local relevance.
- Useful service information.
- Consistent business information.
- Appropriate local context.
The objective is quality geographic relevance, not geographic keyword multiplication.
Step 8 — Earning Backlinks to AI-Relevant Pages
The campaign also incorporated authority building through backlinks and external references.
Generative search does not make conventional authority irrelevant.
In fact, the broader digital ecosystem still matters.
Why Authority Still Matters
A business needs more than content.
It needs credibility.
Relevant external references can contribute to the overall authority and recognition of the brand.
Potential authority-building activities can include:
- Relevant backlinks.
- Industry references.
- Local mentions.
- Digital PR.
- Relevant citations.
The emphasis should remain on relevance and quality, rather than acquiring links purely for volume.
Supporting Important Pages
Strategically important pages can benefit from external authority.
For example, if emergency plumbing is a core commercial service, relevant authority signals can support the broader ecosystem around emergency plumbing content.
Likewise, pages addressing major local service topics can become stronger assets when supported by relevant external references.
From Ranking a Keyword to Building an Entity
This is one of the biggest conceptual changes introduced by GEO.
Traditional SEO can focus heavily on:
“How do we rank this keyword?”
Generative search encourages another question:
“How do we build a recognizable, relevant, and authoritative business entity?”
That shift influenced the Everest campaign.
Results After Implementation: ChatGPT Presence
The results section is where the strategy becomes tangible.
The campaign testing showed Everest Drain & Plumbing appearing in ChatGPT recommendations for multiple high-intent Toronto plumbing queries.
Result 1 — “Top 10 Plumber in Toronto”
During the testing represented in the case-study material, Everest Drain & Plumbing appeared in position #2 on ChatGPT for:
“Top 10 Plumber in Toronto”
This is a significant type of query because it combines:
- Local intent.
- Commercial intent.
- Recommendation intent.
- Category-level discovery.
The user is not asking what plumbing is.
They are asking for a shortlist of providers.
That makes inclusion in the answer particularly relevant from a visibility and consideration perspective.
Everest appearing second in the generated recommendation list meant the business was not simply present somewhere in an information response. It was included within a recommendation-oriented result.
Result 2 — “Who Are the Best Plumbers in Toronto Right Now?”
The campaign also showed Everest Drain & Plumbing appearing in position #2 for:
“Who are the best plumbers in Toronto right now?”
This query is even more conversational.
It contains:
- A recommendation request.
- A location.
- A category.
- A current-selection implication.
The user wants to know who they should consider now.
That makes the appearance of Everest particularly relevant to the campaign’s objective.
What the ChatGPT Results Demonstrate
The two observed results illustrate several strategic outcomes.
Visibility beyond traditional SERPs
The brand was being surfaced in an AI-generated environment rather than solely through conventional organic listings.
Brand discoverability
The business became part of the answer set for specific local questions.
Recommendation-level visibility
The queries were recommendation-oriented rather than purely informational.
Alignment with conversational search
The prompts resembled the natural language customers may use when asking an AI assistant for help.
The results should not be interpreted as a permanent or universal “#2 ranking on ChatGPT.” AI outputs can vary. Instead, they demonstrate that the implemented strategy was associated with observed inclusion in relevant generative search responses during the campaign testing.
Results After Implementation: Claude Presence
The campaign also tested visibility within Claude.
Two particularly relevant queries produced observed visibility for Everest Drain & Plumbing.
“Best Emergency Plumber in Toronto 24/7”
Everest appeared in position #4 on Claude for:
“Best emergency plumber in Toronto 24/7”
This query is valuable because it contains multiple high-intent signals.
Emergency intent
The user may have an immediate plumbing problem.
Local intent
The customer wants a provider in Toronto.
Commercial intent
The user is looking for a service provider.
Availability intent
The user specifically wants 24/7 service.
A business that appears for such a query is entering a very different stage of the customer journey than one appearing for a generic informational plumbing question.
“Best Company for Basement Flood Prevention in Toronto”
Everest also appeared in position #4 for:
“Best company for basement flood prevention in Toronto”
This query demonstrates another aspect of the strategy.
It is not simply about a generic plumbing service.
It is a problem-based query.
The customer is thinking about a specific risk—basement flooding—and wants a company capable of addressing it.
That means topical relevance becomes important.
Content around flood prevention, plumbing risks, drainage, and related services can help create a stronger relationship between the business and the problem being searched.
Why Position #4 Is Still Valuable
AI recommendations should not be interpreted exactly like conventional organic rankings.
The important point is that Everest entered the consideration set generated by Claude for highly relevant local queries.
For a customer looking for an emergency plumber or flood-prevention company, being included in the generated recommendations can create an opportunity for consideration that would not exist if the business were absent from the response.
Results After Implementation: Gemini Presence
Gemini produced two of the strongest observed results in the campaign.
“Trusted Local Plumbers Near Downtown Toronto”
During the testing represented in the campaign, Everest appeared in position #1 on Gemini for:
“Trusted local plumbers near downtown Toronto”
This is a highly specific local recommendation query.
It combines:
- Trust.
- Service.
- Locality.
- Proximity.
- Recommendation intent.
The first-position appearance therefore represents strong observed visibility for this particular prompt.
Why the Trust Modifier Matters
The word “trusted” changes the intent.
The user is not merely asking:
“Who is a plumber?”
They are asking:
“Which plumber should I feel comfortable considering?”
This reinforces the importance of content and digital signals that establish the business’s identity, services, expertise, and local relevance.
“Affordable Plumbing Services in Toronto With Upfront Pricing”
Everest also appeared in position #1 for:
“Affordable plumbing services in Toronto with upfront pricing”
This is another highly commercial query.
It contains:
- Affordability.
- Pricing transparency.
- Local relevance.
- Service intent.
The user is likely further along in the decision-making process than someone searching simply for “plumbing.”
They already have a service need and are establishing the criteria by which they want to choose a provider.
Why These Queries Are Valuable
The campaign’s Gemini results demonstrate why query angle matters.
Two businesses may offer the same basic service.
But a user may choose between them based on:
- Availability.
- Location.
- Trust.
- Pricing.
- Experience.
- Specific expertise.
Generative search can reflect these distinctions in the questions users ask.
Therefore, a strong GEO strategy needs to cover multiple decision criteria, not just one broad service keyword.
The Cross-Platform AI Visibility Matrix
The campaign produced observed visibility across three major AI environments.
| AI Platform | Search Query | Observed Position |
| ChatGPT | Top 10 Plumber in Toronto | #2 |
| ChatGPT | Who are the best plumbers in Toronto right now? | #2 |
| Claude | Best emergency plumber in Toronto 24/7 | #4 |
| Claude | Best company for basement flood prevention in Toronto | #4 |
| Gemini | Trusted local plumbers near downtown Toronto | #1 |
| Gemini | Affordable plumbing services in Toronto with upfront pricing | #1 |
These results are based on the campaign testing represented in the supplied case-study material.
What the Results Tell Us
1. Visibility occurred across multiple AI platforms
Everest was not limited to a single generative search environment.
2. Different query types produced visibility
The queries included:
- Recommendation searches.
- Emergency searches.
- Problem-based searches.
- Trust-based searches.
- Price-focused searches.
- Local discovery searches.
3. Local intent was consistently important
Toronto and downtown Toronto were recurring geographic signals.
4. Service specificity mattered
The results were not restricted to the broad term “plumber.”
They extended to emergency plumbing and flood prevention.
5. Commercial modifiers mattered
Words such as:
- Best.
- Affordable.
- Trusted.
- 24/7.
provided important context.
6. Search intent created multiple visibility opportunities
The campaign did not rely on one query.
Instead, it created a broader set of opportunities around different customer needs.
What Changed: From Keyword Ranking to AI Recommendation Visibility
The strategic transformation can be summarized simply.
Before
- Low organic visibility.
- Limited clicks and impressions.
- Core terms lacked strong first-page visibility.
- Limited AI presence.
After
- Observed AI recommendation visibility.
- ChatGPT presence.
- Claude presence.
- Gemini presence.
- Visibility across multiple local and service-specific prompts.
But the most important change was conceptual.
Traditional SEO asks:
“Where does the website rank?”
Generative search asks:
“Does the system understand the business well enough to include or recommend it when a relevant user asks?”
The second question does not replace the first.
It expands it.
A business still needs strong technical SEO, useful content, relevant pages, authority, and local optimization.
But it also needs to think about how all of those signals combine to create a recognizable digital entity.
Why Search Intent Was the Foundation of the Campaign
The campaign’s 3 C framework—Content Type, Content Format, and Content Angle—was more than a content-planning exercise.
It provided a way to translate customer questions into optimization opportunities.
Content Type
First determine what type of asset should answer the question.
For example:
“Best emergency plumber in Toronto 24/7”
requires a service-oriented experience.
“How can I prevent basement flooding?”
may require an informational guide.
“Affordable plumbing services in Toronto with upfront pricing”
requires commercial information.
Content Format
Next determine how the answer should be presented.
For recommendation intent, structured lists and comparisons can be useful.
For problem-solving intent, step-by-step guides may work better.
For direct questions, concise answer blocks and FAQs may be appropriate.
Content Angle
Finally, identify the specific reason the customer is searching.
For:
“Best emergency plumber in Toronto 24/7”
the angle is:
Emergency availability + Toronto + 24/7
For:
“Affordable plumbing services in Toronto with upfront pricing”
the angle becomes:
Affordability + pricing transparency + Toronto
This approach prevents generic content from becoming the default answer to every search.
How AEO, GEO, Local SEO and Traditional SEO Work Together
One of the biggest mistakes businesses can make is treating SEO, AEO, GEO, and local SEO as completely separate disciplines.
They are better understood as interconnected layers.
The campaign’s framework can be summarized as:
SEO → Local SEO → AEO → GEO → Generative Search Visibility
Traditional SEO
Traditional SEO supports:
- Crawlability.
- Relevance.
- Rankings.
- Organic traffic.
Without a solid technical and content foundation, AI optimization has limited value.
Local SEO
Local SEO strengthens:
- Geographic relevance.
- Local discovery.
- Service-area visibility.
For Everest, Toronto and GTA relevance was essential.
AEO
AEO focuses on:
- Answer relevance.
- Question coverage.
- Direct response potential.
It asks whether the content actually addresses the questions users are asking.
GEO
GEO focuses on:
- Generative search visibility.
- Entity understanding.
- AI recommendation potential.
It expands the optimization objective beyond conventional ranking.
Why They Should Work Together
Consider a single customer journey:
User asks:
“Who is the best emergency plumber in Toronto 24/7?”
The business needs:
- A technically accessible website.
- A relevant emergency plumbing page.
- Toronto-specific context.
- Clear service information.
- Strong internal links.
- Supporting content.
- Relevant FAQs.
- Clear business/entity information.
- Appropriate structured data.
- Authority signals.
No single tactic creates that ecosystem.
The result comes from integration.
The Role of Entity Optimization in Generative Search
Generative search makes entity understanding increasingly important.
An entity can be thought of as a clearly recognizable real-world subject—such as a business, organization, place, service, or product.
For Everest, the goal was to strengthen the digital relationship between the brand and its core characteristics.
Everest as a Search Entity
The business should be clearly associated with:
- Plumbing.
- Toronto.
- GTA.
- Emergency services.
- Drain services.
- Residential plumbing.
- Commercial plumbing.
- Flood prevention.
The more consistently these relationships are established, the clearer the overall business context becomes.
Strengthening Entity Relationships
Relevant mechanisms include:
- Consistent brand naming.
- Service pages.
- Location content.
- Structured data.
- Internal linking.
- External references.
- Supporting content.
For example:
Everest Drain & Plumbing
→ Emergency Plumbing
→ Toronto
→ 24/7 Service
is a much clearer relationship than a generic mention of the company on an unrelated page.
Likewise:
Everest Drain & Plumbing
→ Basement Flood Prevention
→ Toronto
creates relevance around a particular customer problem.
The objective is to make these relationships clear throughout the website and broader digital presence.
Why Conversational Search Queries Became Critical to the Campaign
Generative search changes how users express their needs.
A traditional keyword might be:
“plumber Toronto”
A conversational query might be:
“Who are the best plumbers in Toronto right now?”
A recommendation query might be:
“Who are trusted local plumbers near downtown Toronto?”
A commercial query might be:
“Affordable plumbing services in Toronto with upfront pricing.”
An emergency query might be:
“Best emergency plumber in Toronto 24/7.”
The key difference is that the longer query provides additional context.
It tells the system what the customer values.
That means modern optimization must account for query meaning, not simply exact-match keywords.
Why Longer Queries Can Reveal More Intent
Consider:
“plumber Toronto”
We know:
- Service.
- Location.
But consider:
“affordable plumbing services in Toronto with upfront pricing”
Now we know:
- Service.
- Location.
- Price sensitivity.
- Pricing transparency.
- Commercial intent.
Or:
“best emergency plumber in Toronto 24/7”
Now we know:
- Service.
- Location.
- Urgency.
- Availability.
- Recommendation intent.
These additional signals provide valuable direction for content strategy.
Measuring Generative Search Visibility: What Businesses Should Track
As search changes, measurement must evolve too.
Traditional SEO metrics remain important, but they should be complemented by metrics designed to evaluate AI visibility. The campaign framework identifies traditional SEO metrics alongside AEO and GEO-oriented measurements.
Traditional SEO Metrics
Businesses can continue monitoring:
- Rankings.
- Organic impressions.
- Organic clicks.
- CTR.
- Organic conversions.
These remain essential because conventional search still drives significant discovery.
AEO Metrics
For answer-focused optimization, useful measurements can include:
- Question visibility.
- Answer presence.
- FAQ performance.
- Answer coverage.
The objective is to understand whether important questions are being addressed effectively.
GEO Metrics
Generative search introduces additional considerations:
- AI mentions.
- Recommendation inclusion.
- Observed AI position.
- Platform coverage.
- Query coverage.
- Brand context where measurable.
These metrics should be treated carefully because generative systems do not behave exactly like traditional ranking systems.
Cross-Platform Visibility
A business can build a testing matrix across:
| Metric | ChatGPT | Claude | Gemini |
| Highest observed position | #2 | #4 | #1 |
| Local queries tested | ✓ | ✓ | ✓ |
| Commercial queries tested | ✓ | ✓ | ✓ |
| Recommendation visibility | ✓ | ✓ | ✓ |
The objective is not to claim that an AI platform has a permanent ranking position.
Instead, the objective is to establish:
Which relevant prompts produce visibility, on which platforms, and under what conditions?
That is a much more useful approach to generative search measurement.
What This Case Study Teaches Local Service Businesses About AI Search
The Everest campaign provides several important lessons for local businesses preparing for the next phase of search.
Lesson 1 — Ranking Is No Longer the Only Visibility Objective
Traditional rankings remain valuable.
But customers can now discover businesses through AI-generated recommendations.
A complete visibility strategy should therefore consider both conventional search results and generative search environments.
Lesson 2 — Search Intent Must Come Before Content Production
Publishing content without understanding intent can create volume without relevance.
The 3 C framework demonstrates a better approach:
Content Type + Content Format + Content Angle
The question is not:
“What can we publish?”
It is:
“What does the customer need to know at this moment?”
Lesson 3 — Local Relevance Matters in AI Recommendations
For a Toronto plumbing company, geographic context is fundamental.
The campaign’s strongest queries consistently included local signals.
That reinforces the importance of combining:
Service + Location + Intent
Lesson 4 — Brand Entity Signals Matter
The business must be recognizable as more than a domain name.
Its services, location, expertise, and customer problems need to form a consistent digital identity.
Lesson 5 — Structured Content Makes Information Easier to Understand
Clear headings, tables, FAQs, lists, and concise answer blocks improve content usability.
They also create a more organized information environment.
Lesson 6 — FAQs Can Capture Conversational Demand
Questions are increasingly becoming search queries.
A useful FAQ strategy can turn real customer questions into content assets.
Lesson 7 — Topical Authority Supports AI Visibility
A business should not rely on one page for every subject.
Supporting content creates broader relevance around services and customer problems.
Lesson 8 — AI Search Requires Continuous Testing
Generative responses can change.
Therefore, visibility should be tested repeatedly rather than assumed from one successful prompt.
Lesson 9 — Different AI Platforms Can Produce Different Results
The Everest campaign demonstrated different observed positions across ChatGPT, Claude, and Gemini.
That is why cross-platform testing matters.
Lesson 10 — AEO and GEO Should Complement SEO
The future is not:
SEO versus GEO.
It is:
SEO + AEO + GEO + Local SEO.
That integrated approach creates a stronger foundation for search visibility.
Common Mistakes Businesses Make When Optimizing for Generative Search
The rise of AI search has also created considerable confusion.
Businesses may hear terms such as “GEO,” “LLM SEO,” or “ChatGPT SEO” and assume there is a single technical trick that guarantees AI recommendations.
There isn’t.
Mistake 1 — Treating GEO as Keyword Stuffing
Repeating a phrase such as “best plumber Toronto” dozens of times does not create genuine relevance.
Generative search requires context.
The business needs to be associated naturally with its services, locations, expertise, and customer needs.
Mistake 2 — Creating AI-Generated Content Without Expertise
AI can help with content production, but quantity alone is not a strategy.
Local service content should be:
- Useful.
- Accurate.
- Relevant.
- Original.
- Customer-focused.
For a plumbing business, generic articles that could apply to any business in any city provide limited differentiation.
Mistake 3 — Creating Hundreds of Thin Location Pages
Changing “Toronto” to another city name does not automatically create local relevance.
Location content needs genuine value.
Mistake 4 — Ignoring Traditional Technical SEO
A GEO strategy cannot compensate for fundamental technical problems.
Businesses still need a website that is:
- Crawlable.
- Structured.
- Accessible.
- Internally connected.
- Content-rich.
Mistake 5 — Publishing FAQs Without Search Intent
FAQs should answer real questions.
They should not exist simply because FAQ sections are considered “good for SEO.”
Mistake 6 — Ignoring Local Context
A local business needs to communicate where it operates.
Generic national content may not satisfy a customer looking for a provider in a specific city or neighborhood.
Mistake 7 — Optimizing for Only One AI Platform
An approach focused entirely on one platform can miss opportunities elsewhere.
The Everest campaign’s cross-platform testing demonstrates why ChatGPT, Claude, Gemini, and other generative environments should be evaluated independently.
Mistake 8 — Assuming One AI Result Is Permanent
AI outputs can change.
A business should treat an observed result as a measurement at a particular point in time—not as a permanent ranking guarantee.
Mistake 9 — Focusing Only on Informational Queries
Commercial queries can be particularly valuable.
Questions involving:
- Best.
- Trusted.
- Affordable.
- Emergency.
- Near me.
- 24/7.
can indicate users who are closer to choosing a provider.
Mistake 10 — Measuring Traffic but Not AI Visibility
If the search experience changes, measurement must change too.
Businesses should understand not only how much organic traffic they receive, but also whether they are becoming visible within emerging AI recommendation experiences.
How ThatWare Approaches AEO and GEO Campaigns
The Everest campaign illustrates ThatWare’s broader approach to modern search optimization.
Rather than positioning GEO as a standalone trick, the strategy integrates it into a broader search framework.
Search Intelligence
The process begins with understanding the search environment.
That includes:
- Keyword research.
- Query analysis.
- Competitor analysis.
- Search intent mapping.
The purpose is to identify what users actually search for and what type of answer they expect.
AI Search Optimization
The next layer focuses on:
- Conversational query optimization.
- Answer-focused content.
- Entity optimization.
- Structured content.
The objective is to make important information clear, useful, and contextually connected.
Technical SEO
Technical fundamentals remain part of the process.
That can include:
- Technical audits.
- Schema.
- Crawlability.
- Internal linking.
- Site architecture.
A strong AI search strategy still requires a strong website.
Local Search Optimization
For local businesses, ThatWare’s approach incorporates:
- Local intent.
- Location pages.
- Service-area optimization.
- Local authority.
This creates the geographic context necessary for local discovery.
Generative Search Monitoring
Generative search should be tested, not guessed.
That can involve:
- Prompt testing.
- Platform testing.
- Visibility tracking.
- Recommendation analysis.
Testing allows the strategy to identify where the brand appears, where it does not, and which query categories produce the strongest opportunities.
Integrated SEO + AEO + GEO Strategy
The overall methodology can be expressed as:
SEO + AEO + GEO + Local SEO + Content + Technical SEO
This integrated approach recognizes that generative search does not exist outside the broader search ecosystem.
Why the Everest Drain & Plumbing Campaign Is Different From a Conventional SEO Campaign
A conventional SEO campaign may primarily focus on:
- Keywords.
- Rankings.
- Traffic.
- Backlinks.
Those remain relevant.
But the Everest campaign expanded the objective toward:
- Searcher intent.
- Conversational queries.
- AI answers.
- Brand/entity relevance.
- Local recommendations.
- AI platform visibility.
That changes how success is viewed.
Instead of asking only:
“Did we improve the ranking of this keyword?”
the strategy also asks:
“Did we increase the probability that the business is relevant to the questions customers are asking?”
That is the difference between keyword optimization and search ecosystem optimization.
The campaign was designed not merely to help Everest rank for searches, but to make the business more relevant to the questions that customers increasingly ask search and AI systems.
Frequently Asked Questions
What was the main goal of the Everest Drain & Plumbing SEO campaign?
The campaign aimed to improve Everest Drain & Plumbing’s visibility for high-intent local plumbing searches while expanding its presence into generative search environments. The initial challenge included low organic visibility, limited clicks and impressions, and insufficient visibility for important plumbing-related queries.
The strategy combined traditional SEO with AEO, GEO, local optimization, content, structured data, internal linking, FAQs, geo-targeted content, and authority building.
What is Generative Engine Optimization?
Generative Engine Optimization, or GEO, is the practice of optimizing a brand’s digital presence to improve its relevance and potential visibility within AI-generated search experiences.
It considers factors such as entity clarity, topical relevance, semantic relationships, structured information, brand consistency, and supporting authority.
GEO should not be understood as a guaranteed method for obtaining a particular AI ranking.
How did ThatWare optimize Everest Drain & Plumbing for ChatGPT?
ThatWare built an integrated search strategy around search intent, conversational queries, content, internal linking, structured data, geo-targeted information, FAQs, and authority.
The campaign then tested the business across relevant ChatGPT queries.
The supplied case-study results show Everest appearing at position #2 for “Top 10 Plumber in Toronto” and “Who are the best plumbers in Toronto right now?”
Did Everest Drain & Plumbing appear on ChatGPT?
Yes. During the testing represented in the case study, Everest Drain & Plumbing appeared in ChatGPT responses for multiple Toronto plumbing queries.
The observed examples include:
- #2 — “Top 10 Plumber in Toronto”
- #2 — “Who are the best plumbers in Toronto right now?”
These should be treated as observed results from the campaign testing rather than permanent or universal rankings.
What ChatGPT queries showed Everest Drain & Plumbing?
Two documented queries were:
“Top 10 Plumber in Toronto”
and:
“Who are the best plumbers in Toronto right now?”
Everest appeared in position #2 for both queries during the testing represented in the case study.
Did Everest appear in Claude recommendations?
Yes.
The case study documents observed position #4 on Claude for:
“Best emergency plumber in Toronto 24/7”
and:
“Best company for basement flood prevention in Toronto.”
These queries demonstrate visibility for both emergency and problem-based local intent.
Did Everest appear in Gemini recommendations?
Yes.
The case study shows Everest appearing in position #1 on Gemini for:
“Trusted local plumbers near downtown Toronto”
and:
“Affordable plumbing services in Toronto with upfront pricing.”
These represent highly specific local and commercial search intents.
How does AEO differ from traditional SEO?
Traditional SEO focuses heavily on improving a website’s visibility in search results.
AEO focuses more specifically on answering user questions clearly and structuring information so that answer-oriented search experiences can understand and use it effectively.
In practice, the two approaches overlap significantly.
Why is local SEO important for AI search?
Local service recommendations are inherently geographic.
Someone looking for a plumber usually needs a provider who serves their location.
Therefore, city, neighborhood, service area, and local relevance can be important parts of a generative search query.
For Everest, Toronto and GTA relevance was a central component of the strategy.
Can a local business optimize for ChatGPT, Claude and Gemini?
A local business can build a digital ecosystem designed to improve its relevance across generative search platforms.
However, no ethical SEO strategy can guarantee a specific AI platform will always return a business in a specific position.
AI outputs can vary based on the prompt, context, model, location, timing, and other factors.
The appropriate objective is to strengthen the underlying signals of relevance, authority, usefulness, and entity clarity and then measure visibility through consistent testing.
How does structured data support AI search optimization?
Structured data provides machine-readable information about elements of a website and business.
Depending on the context, relevant types may include LocalBusiness, Organization, Service, Breadcrumb, and FAQPage.
Structured data should complement high-quality visible content and a well-organized site. It should not be treated as a standalone mechanism for obtaining AI recommendations.
How can ThatWare help businesses improve generative search visibility?
ThatWare can approach generative search as an extension of a broader SEO strategy.
The methodology can include:
- Search intelligence.
- Keyword and query research.
- Search-intent mapping.
- AEO.
- GEO.
- Entity optimization.
- Technical SEO.
- Local SEO.
- Content optimization.
- Structured data.
- Internal linking.
- AI visibility testing.
The Everest Drain & Plumbing case demonstrates how these elements can be combined into a unified strategy rather than treated as isolated tactics.
The Future of Local Search Is Generative, Conversational and Intent-Driven
The search journey is changing.
For years, businesses optimized around the assumption that customers would type a keyword into a search engine, review a list of results, visit websites, compare options, and make a decision.
That journey still exists.
But another journey is developing.
A customer can now ask:
“Who should I choose?”
instead of simply:
“What businesses exist?”
That distinction is fundamental.
When a user asks an AI system to identify the best plumber, the most trusted provider, the most affordable service, or the best emergency option, the AI interface becomes part of the discovery process.
For businesses, this creates a new visibility objective.
The goal is not simply to appear somewhere online.
The goal is to become relevant, understandable, trustworthy, and discoverable within the questions that matter to potential customers.
That requires a combination of:
- Authority.
- Relevance.
- Trust.
- Structured information.
- Local context.
- Strong content.
- Clear entities.
- Technical accessibility.
- Search-intent alignment.
The Everest Drain & Plumbing campaign illustrates this progression:
Initial challenge
→ Search-intent analysis
→ Keyword and AI query research
→ SEO content
→ AI-focused landing pages
→ Structured data
→ Internal linking
→ FAQs
→ Geo-targeted content
→ Authority building
→ AI platform testing
→ Generative search visibility
The observed results included:
- ChatGPT: #2 and #2
- Claude: #4 and #4
- Gemini: #1 and #1
These numbers are not a promise that every user will receive the same result. Instead, they demonstrate something more strategically important: a local plumbing business can build measurable visibility within multiple generative search environments by aligning its digital presence with search intent and user questions.
Want Your Business to Be Found in ChatGPT, Gemini, Claude and Other AI Search Engines?
Search is no longer limited to Google’s traditional results page.
Potential customers can now use generative AI platforms to discover businesses, compare services, ask for recommendations, evaluate options, and identify providers based on specific requirements.
For local businesses, that creates a new competitive environment.
A customer might ask:
“Who are the best businesses in my city?”
“Who can solve this problem?”
“Which provider is available 24/7?”
“Who offers affordable pricing?”
“Which company is trusted locally?”
If your business is absent from those recommendation environments, you may be missing an increasingly important layer of digital discovery.
The Everest Drain & Plumbing campaign demonstrates how ThatWare approached this challenge by combining SEO, AEO, GEO, local SEO, search-intent analysis, content strategy, structured data, internal linking, geo-targeted content, FAQs, and authority building.
The objective was never to rely on a single keyword or a single AI platform.
It was to create a stronger search ecosystem around the business—one capable of communicating who the business is, what it offers, where it operates, which problems it solves, and why it is relevant to different customer questions.
That is the direction in which modern search is moving.
From keywords to questions.
From rankings to recommendations.
From pages to entities.
From traditional SERPs to a broader generative search ecosystem.
And for businesses prepared to adapt, that shift creates a significant opportunity.
Ready to improve your brand’s visibility across generative search?
Partner with ThatWare for AI SEO, AEO, GEO, LLM visibility, and local search optimization designed around the way customers search today.
