NeverRanked · Page templates

Templates for the 5 page types AI engines cite.

Each template is the page outline + body anchors + schema callouts + word count target the cited competitor pages share in the AI-citation data. Build to this shape, deploy the named schema from the schema library, internally link from existing high-traffic pages, wait 4 to 8 weeks for AI to re-index.

How to use these: your NeverRanked per-query playbook names the intent shape for each weak query (head-demand, long-tail by service, long-tail by geography, long-tail by buyer fit, named-comparison). Match the intent to the template below. The template tells you what to build. The schema library tells you what to deploy on the page. The per-query playbook tells you what specific words to use for your firm. The three layers compose.

What is measured vs what is inferred. The word-count and H2-count targets are derived from NeverRanked's analysis of cited competitor pages across the categories NeverRanked has measured. The required schema types and the page-outline shape are templated from the intent classification surfaced in the per-query playbook. The body anchor advice (e.g., "lead with a direct plain-language answer in the first 100 words") is a combination of measurement observation and generally-accepted AEO best-practice. Treat body-anchor advice as informed prior. Your monthly measurement will confirm what specifically moves AI citations for your category.

Where the line is. NeverRanked publishes the templates as a spec. The customer's writer (or the customer's agency) writes the actual copy that fills the template, names the firm's specific services, captures the buyer's actual voice, and ships the page. NeverRanked does not write the body copy, does not have access to the customer's CMS, and does not deploy the page. The boundary is structural: we measure, we hand off a spec, the customer or agency executes. That separation is the engagement.

Templates in this library

Service-specific landing page. For long-tail-by-service queries.
Neighborhood / location landing page. For long-tail-by-geography queries.
Audience-fit landing page. For long-tail-by-buyer-type queries.
Category-leader / comparison hub page. For head-demand queries.
Named-partner / named-attorney page. For named-expertise queries.

Service-specific landing page

For long-tail-by-service queries like "fee-only tax advisor Honolulu" or "estate planning attorney Hawaii." These queries are where buyers have a specific service need and AI rewards pages that name the exact service variant, the deliverables, and the buyer fit.

Service page template

Target words: 1,200 to 1,800 (median 1,500)
H2 count: 6 to 8
Schema: Service, FAQPage, Offer (if pricing visible), LocalBusiness (provider reference), BreadcrumbList
FAQ block: Required. 5 to 8 questions specific to this service.
Internal links: From homepage services menu, from related service pages, from the firm's about/team page

H1: [The specific service named exactly as a buyer would search it]

Example: "Fee-only tax planning for Hawaii small business owners"

H2: What this service covers

Lead with deliverables, not the firm's history. 100-150 words.

H2: Who this service is for

Named buyer audience. Use their language, not yours.

H2: Our process

Numbered steps. AI extracts list-shaped content well.

H3: Step 1: Discovery

H3: Step 2: Analysis

H3: Step 3: Implementation

H3: Step 4: Ongoing

H2: Pricing

Even a range. "Starts at $X for [scope]." AI rewards transparency.

H2: What you get (deliverables)

Bulleted list. Concrete artifacts the buyer receives.

H2: Frequently asked questions

5-8 questions buyers actually ask. Match the FAQPage schema verbatim.

H2: How to get started

One specific next step (book a call, fill a form). No multi-option CTA.

Required body anchor: the first 100 words Open the page with a direct, plain-language answer to the query. "[Firm] offers [exact service name] for [audience] in [geographic area]. The engagement covers [3-5 deliverables in plain language]. Pricing starts at [range]. Most engagements take [duration]."

Required body anchor: deliverables as a structured list Not prose. A bulleted or numbered list of what the buyer receives. AI engines extract this directly into answers.

Required body anchor: a named provider At least one paragraph naming the principal or lead person who delivers this service. Cross-references with Person schema and your About page. AI engines preferentially cite pages with named professionals over anonymous service pages.

Neighborhood / location landing page

For long-tail-by-geography queries like "CPA in Bishop Street" or "wealth advisor Kakaako." These are won by giving AI engines an unambiguous location signal (schema + visible address + neighborhood naming) and explaining why the neighborhood matters to the buyer.

Geography page template

Target words: 800 to 1,200 (median 1,000)
H2 count: 5 to 7
Schema: LocalBusiness with full PostalAddress + GeoCoordinates, Place (neighborhood), OpeningHoursSpecification per day, BreadcrumbList
FAQ block: Optional. 3-5 geo-specific questions if relevant.
Internal links: From homepage location menu, from contact page, from any service page that mentions the area

H1: [Firm name] in [Neighborhood / area / landmark]

Example: "Hawaii CPA office in downtown Honolulu"

H2: Our [Neighborhood] location

Full street address. Parking, transit access, accessibility notes.

H2: Why we serve [Neighborhood]

Brief context on the buyer base or business density in this area.

H2: Services we offer at this location

List with internal links to service pages.

H2: How to reach us

Address (again), phone, email, map embed if applicable.

H2: Hours and availability

Match OpeningHoursSpecification schema verbatim.

H2: Other locations we serve

Internal links to other geography pages.

Required body anchor: the address in plain text Full street address must appear in body text, not just schema. AI engines cross-check the two. Example: "Our [Neighborhood] office is located at 1234 Bishop Street, Suite 500, Honolulu, HI 96813."

Required body anchor: at least one named neighborhood landmark Naming a landmark (a specific building, intersection, or public space) lets AI confirm geographic specificity. Example: "Two blocks from the Pacific Guardian Center" or "Across from the Bishop Square fountain."

Required body anchor: opening hours in visible text Match the OpeningHoursSpecification schema. "We are open Monday through Friday, 8 AM to 5 PM. Closed weekends and federal holidays."

Audience-fit landing page

For long-tail-by-buyer-type queries like "Hawaii CPA for real estate investors" or "wealth advisor for retiring physicians." These pages win by demonstrating you understand the audience's specific needs, using their own language, and naming the pain points generic pages miss.

Audience-fit page template

Target words: 1,000 to 1,500 (median 1,250)
H2 count: 5 to 7
Schema: Service with audience block, FAQPage, Article if case study included, LocalBusiness reference
FAQ block: Required. Audience-specific questions, not generic service questions.
Internal links: From homepage audience menu, from related audience pages, from service pages

H1: [Firm] for [Specific buyer type]

Example: "Hawaii tax planning for real estate investors"

H2: Why [buyer type] need specialized [service]

Name 2-3 specific pain points generic service doesn't address.

H2: How our service is structured for [buyer type]

What's different from the generic engagement. Use audience-specific terminology.

H2: Common situations we handle

3-5 named scenarios. Concrete, not abstract.

H2: Representative engagement

Anonymized case study. 200-400 words. The situation, what we did, the outcome.

H2: Questions [buyer type] commonly ask

FAQ block in the audience's voice. Real questions, not marketing-shaped questions.

H2: How to get started

One specific next step.

Required body anchor: the audience named in the first 100 words AI engines cite pages that visibly target a named audience over generic pages. The buyer-type name must appear in the opening paragraph, not just the H1.

Required body anchor: at least one audience-specific term Use language only this audience uses. For real estate investors: "1031 exchange," "depreciation recapture," "passive activity loss." For physicians: "PSLF," "income-driven repayment," "professional corporation." AI engines weight trade-language matches heavily for audience-fit queries.

Optional but powerful: a representative case study Anonymized engagement story. The situation, the work, the outcome. Wrap in Article schema with named author. Even anonymized, these compound in training-data engines over time.

Category-leader / comparison hub page

For head-demand queries like "best CPA in Honolulu" or "top wealth advisor Hawaii." These are the hardest to win because they reward broad authority signals. The page itself is necessary but not sufficient. It works best paired with editorial mentions on third-party surfaces (publications, directories).

Category-leader page template

Target words: 1,500 to 2,200 (median 1,800)
H2 count: 7 to 10
Schema: LocalBusiness, multiple Service blocks, FAQPage, AggregateRating (if verifiable), BreadcrumbList
FAQ block: Required. 6 to 10 questions covering the full category buyer journey.
Internal links: From homepage hero, from every service page, from every audience page

H1: [Direct, query-shaped title]

Example: "Honolulu tax accounting for businesses and individuals"

H2: What this category covers

Plain-language overview. What buyers should know before choosing.

H2: The criteria that separate strong providers from weak ones

3-5 named criteria. Concrete, evaluable.

H2: How we meet each criterion

Match the criteria above, with proof points.

H2: Services we provide

Bulleted list with internal links to each service-specific page.

H2: Who we work with

Bulleted list with internal links to each audience-fit page.

H2: Our team

Brief mention of named principals with links to their Person pages.

H2: Frequently asked questions

6-10 questions. The category buyer journey from awareness to commitment.

H2: How to get started

One specific next step.

Required body anchor: the buyer's selection criteria Name 3-5 specific things buyers should evaluate when choosing in this category. AI engines preferentially cite pages that help the buyer decide, not pages that just promote the firm.

Required body anchor: comparison-friendly framing Where you stand on each criterion, not just claims. "We specialize in X but do not handle Y. For Y, we refer to [type of firm]." Honest scope is more cited than puffery.

Required: deep internal linking Link to at least 3 service-specific pages, 2 audience-fit pages, and the team page. Category-leader pages function as hubs. AI engines reward strong internal link structure.

Named-partner / named-attorney page

For named-expertise queries like "[name] Honolulu attorney" or "Hawaii CPA who specializes in [subject]." Critical for high-trust professional services categories (law, wealth, CPA) where AI engines preferentially cite specific named professionals. Slower to compound but most defensible in training-data engines (Claude, Gemma) over years.

Named-partner page template

Target words: 800 to 1,400 (median 1,100)
H2 count: 5 to 7
Schema: Person with knowsAbout + hasCredential + sameAs, worksFor reference to LocalBusiness, optionally Article references for published work
FAQ block: Optional. Person-specific questions if there are recurring buyer asks.
Internal links: From homepage team menu, from every service page this person delivers, from any Article they authored

H1: [Full name], [Credential] | [Role]

Example: "Jane Smith, CPA | Partner, Tax Practice"

H2: What [name] specializes in

Match the knowsAbout schema. Specific subject areas, not generic categories.

H2: Background and credentials

Education, certifications, professional memberships. Cross-references hasCredential schema.

H2: How [name] works with clients

Process, philosophy, communication style. Audience-specific if relevant.

H2: Recent work

Published articles, speaking engagements, case studies, named press mentions. Link to Article schema entries.

H2: Professional profiles

LinkedIn, Bar association, professional directory links. Match sameAs schema.

H2: Contact [name]

Direct email, scheduling link, or assistant contact. Specific, not "contact our firm."

Required body anchor: specialty areas in plain text The knowsAbout schema list also appears in body text, verbatim. AI engines cross-check schema against body content. Mismatched signals get downweighted.

Required body anchor: named credentials with issuing body Not just "CPA" but "Certified Public Accountant (Hawaii Society of CPAs, 2014)." AI engines weight credentials with issuing bodies higher than bare letters.

Required: external profile links LinkedIn at minimum. For attorneys: state bar, Chambers, BestLawyers, Justia. For CPAs: AICPA, state society. For wealth advisors: SEC IAPD, CFP Board. These compound in training-data engines.

Compounding asset: published work Even one named-byline article per year, wrapped in Article schema and authored to this Person, builds training-data presence over multiple model refresh cycles. This is the slowest AEO surface and the most defensible once it forms.

How the templates compose

A firm's site does not need just one template. A complete AEO foundation typically deploys:

One category-leader page at the root (e.g., /services/ or the homepage itself)
One service page per distinct service variant the firm offers (typically 4 to 12)
One geography page per office location (1 to N)
One audience-fit page per major buyer type the firm specializes in (typically 2 to 6)
One named-partner page per principal (typically 1 to N)

For a mid-sized firm with 5 services, 2 offices, 3 audience types, and 4 partners, that's 1 + 5 + 2 + 3 + 4 = 15 pages built to template. A 3-6 month buildout for an in-house team or agency executing against the NeverRanked punch list. The pages compound: AI engines reward sites where multiple templates interlink correctly.

The relationship to the schema library

Templates tell you what to build. The schema library tells you what to deploy on the page. The per-query playbook in your NeverRanked appendix tells you which template applies to which weak query and what specific words to use. The three layers compose:

NeverRanked playbook: "This weak query is long-tail-by-service. Build a service page for [service variant]."
Templates (this page): "Here is the service-page outline, word count, body anchors, and structural rules."
Schema library: "Here is the deployable JSON-LD for Service + FAQPage + Offer + LocalBusiness, ready to copy."

What happens next

Build the page to the template (outline + body anchors).
Deploy the schema from the schema library.
Internally link from existing high-traffic pages so crawlers find the new page within the first re-index cycle.
Validate the schema at validator.schema.org.
Wait 4 to 8 weeks for AI engines to re-index. Training-data engines (Claude, Gemma) take longer.
Verify in your monthly NeverRanked measurement that the page is now being cited on the target queries.

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