AEO/GEO Services: What Agencies Should Include
Naqui ShaikhAEO/GEO SERVICES · PIERVIEW.AI
One of the reasons the AEO and GEO market is so confusing right now is that two agencies can both call themselves AEO/GEO providers and be doing almost completely different work. One is repackaging existing SEO deliverables with a new label. The other is running structured citation audits, rebuilding content architecture for answer extraction, building off-site authority across the sources AI engines actually trust, and tracking share of voice across five different AI engines every month.
The gap between those two is not a gap in vocabulary. It is a gap in what a client actually receives and whether that work produces the results they are paying for.
This article is a clear, practical breakdown of what a serious AEO/GEO service should actually include, organized by category, with notes on what good looks like and where agencies frequently cut corners.
Table of Contents
- The Six Core Service Components
- What a Good Monthly Deliverable Set Actually Looks Like
- What Agencies Often Leave Out
- FAQ
- Summary
The Six Core Service Components
1. AI Visibility Audit
Everything starts here. Before optimizing anything, you need to know where the client actually stands across AI engines right now. A real AI visibility audit is not a one-page summary. It covers:
Prompt research and coverage. What questions are buyers in this client's category actually typing into ChatGPT, Perplexity, and Google AI Overviews? Which of those prompts trigger the client's brand? Which trigger competitors? This requires running a meaningful number of prompts, not five or ten, but enough to represent the real category.
Share of voice baseline. What percentage of relevant prompts mention the client versus competitors? Where are they showing up in responses, first, third, buried at the end? This is the number that everything else is measured against.
Citation analysis. Which of the client's pages, if any, are being cited as sources in AI responses? Which competitor pages are being cited instead? What is the gap?
Technical crawlability. Ensuring AI crawlers like GPTBot and PerplexityBot are not blocked is a critical technical step. Many clients are inadvertently blocking the very bots that would allow AI engines to index and cite their content. Useomnia
Schema audit. What structured data does the client currently have in place? What is missing? Where do gaps in schema explain gaps in AI citation?
Competitor citation mapping. Which competitor domains are being cited most often in the client's category? What content on those domains is being referenced? This is the gap map that drives the content strategy.
The audit is also the most important sales tool in this service. A client who sees their own numbers, especially next to a competitor who is dominating the AI visibility picture, does not need to be sold on the category. They need to be helped.
2. Content Optimization for AI Extraction
This is the work that most agencies calling themselves AEO providers are actually doing, or trying to do. The challenge is that writing for AI citation is genuinely different from writing for search rankings, and agencies who do not understand that difference produce content that ranks fine but never gets cited.
AEO-optimized content tends to be modular and query-specific. You are writing for a machine that is looking for a clean, extractable answer. That means FAQ sections with structured schema markup, concise definitions, step-by-step processes with numbered structure, and headers framed as direct questions. g2
In practice this breaks down into four types of content work:
Answer-first restructuring. Taking existing pages and reformatting them so they lead with a direct answer to the question the page is meant to address, rather than burying the answer after three paragraphs of context. AI engines extract the first clear answer they find. If it is buried, they often move on.
Definition blocks. Short, standalone, citable definitions for the core concepts in a client's category. These are among the most frequently cited content types across LLMs, because they answer "what is X" cleanly and unambiguously.
FAQ sections. Structured question and answer pairs that mirror the actual prompts buyers are typing. Not generic FAQ content, but questions drawn from the prompt research in the audit phase.
Long-form comprehensive coverage. GEO relies heavily on content authority signals, citation frequency across authoritative domains, entity recognition, topical depth, and the degree to which your content demonstrates genuine expertise rather than surface-level coverage. Thin content that does not go deep enough on a topic does not get cited by AI engines that are synthesizing answers from multiple sources. The standard for depth is genuine comprehensiveness, not word count. g2
New content creation fits here too, specifically content targeting the prompt gaps identified in the audit phase, pages that answer questions the client is currently losing to competitors.
3. Schema Markup and Structured Data Implementation
If there is a single technical investment that pays dividends across both traditional SEO and AEO simultaneously, it is structured data implementation. Pierview AI
Schema markup is the technical layer that signals to both traditional and AI search systems that content is answer-ready. The specific schema types that matter most for AEO/GEO:
FAQPage schema. Applied to pages with question and answer structures. Increases the likelihood of content being used in AI Overviews and featured snippets for direct question prompts.
Article schema. Applied to blog posts, guides, and long-form content. Helps AI engines understand the authorship, publication date, and topical focus of content before citing it.
HowTo schema. Applied to step-by-step process content. Particularly effective for instructional queries where buyers are asking how to do something.
Organization schema. Applied to the client's main website and About page. Establishes entity identity, what the brand is, what it does, and where it operates, which affects how AI engines describe the brand when recommending it.
Speakable specification. For enterprise sites, Speakable specification across page types is part of large-scale schema deployment. This signals specifically which content is appropriate for voice assistant responses. Trustpilot
Schema implementation is technical work that requires developer access or CMS-level schema tooling. Agencies that hand clients a set of schema recommendations without implementing them are leaving the most actionable step undone.
4. Citation and Off-Site Authority Building
This is the component that most agencies starting out in AEO/GEO underweight or skip entirely, and it is one of the most important.
GEO pricing is distinct from AEO pricing because the work happens largely off the client's website. GEO involves building the cross-source brand presence that AI models use when generating consensus answers. Trustpilot
AI engines do not form opinions about a brand from that brand's own website alone. They synthesize across multiple sources, and if those sources are consistent and credible, the brand gets cited more frequently and more confidently. If those sources are sparse, inconsistent, or contradictory, the brand either does not appear or appears with caveats.
What this work actually involves:
Wikipedia and knowledge panel consistency. Where applicable, ensuring accurate, consistent entity information exists in the sources that AI engines weight most heavily. This is not always achievable for every client, but where it is applicable it has outsized impact.
Industry publication mentions. Earning genuine editorial placements in the publications that AI engines treat as authoritative sources in a given category. Mid-market and enterprise AEO retainers at $5,000 or more per month typically include digital PR as a core service component, because digital PR earns the authoritative third-party mentions that AI engines use as citation sources. SeaRanks
Review platform consistency. Accurate and consistent brand information across Google Business Profile, G2, Capterra, Trustpilot, and the category-specific review platforms that AI engines pull from.
Reddit and community presence. Building authority signals through trusted mentions, reviews, Reddit, and relevant third-party sources is increasingly important as AI engines weight community content in their responses. This does not mean buying Reddit mentions. It means genuinely earning presence in conversations where buyers are comparing options.
Co-citation analysis and monitoring. Tracking which other domains appear alongside the client's as sources in AI responses. This reveals the competitive set in AI's view and identifies the specific domains whose citation patterns the strategy should be working toward.
5. Technical Foundations
Beyond schema, there are several technical elements that determine whether AI engines can even access and process a client's content correctly.
Crawler access configuration. Fixing technical blockers including robots configurations, canonicals, sitemap coverage, schema, and indexability is foundational work. A client with a robots.txt file that inadvertently blocks GPTBot or PerplexityBot will see little to no improvement from any of the other work until that is fixed.
llms.txt implementation. A simple but increasingly important signal. Only 7.4% of Fortune 500 companies have implemented llms.txt, the basic GEO readiness signal. This is low-effort, high-signal work that most clients have not done. Trustpilot
Page speed and crawlability. The same technical foundations that matter for traditional SEO also matter for AI crawler access. Slow pages, broken internal links, and poor crawl architecture all reduce how thoroughly AI engines can index and process a client's content.
Entity clarity and knowledge graph signals. Structured, consistent information about who the client is, what they do, who they serve, and where they operate, implemented consistently across owned and third-party sources, helps AI engines build a confident model of the brand. Inconsistency across these signals produces confused or absent AI representations of the brand.
6. AI Visibility Tracking and Reporting
None of the above is defensible to a client without measurement, and measurement in this context means something different from a monthly keyword ranking report.
What a proper AEO/GEO reporting stack covers:
Share of voice across AI engines. The percentage of tracked prompts that mention the client, broken down by engine and by prompt intent category. This is the headline metric.
Citation share. How often the client's domain is referenced as a source in AI responses, not just mentioned in the answer body. Citation share is a leading indicator of future visibility growth.
Prompt coverage. How many of the tracked prompts trigger the client's brand at all, and how that expands or contracts month over month.
Competitive benchmarking. How the client's share of voice compares to the two or three closest competitors across the same prompt set. This is what keeps clients emotionally invested in the service, because competitive context makes abstract numbers concrete.
Source co-citation. Which other domains appear alongside the client's as sources. Changes in co-citation patterns often predict changes in visibility before those changes show up in the share of voice numbers.
AI referral traffic. Direct sessions from ChatGPT, Perplexity, Gemini, Claude, and Copilot, segmented by engine in GA4. This ties AI visibility data to the client's existing analytics, making the connection to business outcomes visible.
Pipeline attribution. Measure citation rate, share of voice versus competitors in the same answers, AI referral traffic, and entity confidence. These reflect real visibility in AI. Keyword rankings alone no longer show whether buyers find you in AI tools.
A note on how this data gets collected: it matters more than most agencies realize. Many tracking tools query official AI engine APIs rather than simulating real user sessions. API responses frequently differ from what a real buyer sees when they open ChatGPT and type a question, different citations, different rankings, sometimes different recommendations. Browser-based data collection, which simulates actual user sessions, produces numbers that reflect reality rather than an approximation of it. This is the distinction that separates tracking tools built for show from tracking tools built for decisions.
What a Good Monthly Deliverable Set Actually Looks Like
For a mid-market Growth tier client, a realistic monthly deliverable set covers:
- Prompt tracking across 150 to 300 prompts, updated monthly as language and competitors shift
- 4 to 6 pieces of new or restructured content targeting the highest-value prompt gaps
- Schema updates and new schema implementation on content published that month
- One citation building activity, whether outreach, PR placement, or directory consistency work
- Monthly share-of-voice report with competitive benchmarking, citation share, prompt coverage trends, and AI referral traffic from GA4
- A short written summary of what moved, why, and what the plan is for next month
The report should be something you can hand a client without reformatting. If producing the monthly report takes more than an hour of cleanup, the reporting infrastructure is not set up correctly.
What Agencies Often Leave Out
Off-site citation work. This is the most commonly skipped component. Content optimization and schema alone are necessary but not sufficient for GEO. If AI engines are not seeing consistent, authoritative third-party coverage of the client, citation share stays low regardless of how well-structured the client's own pages are.
Prompt research that is specific to the client's actual buyers. Generic prompt sets produce generic results. The prompt research should be grounded in how real buyers in the client's specific category actually ask questions, which requires real research rather than a templated list.
Competitor citation analysis. Knowing where you are is half the picture. Knowing specifically which competitor pages are being cited and why is what drives an actionable content and citation strategy rather than general optimization.
Clear success metrics defined at the start. Too many agencies start this work without defining what success looks like at ninety days, six months, and a year. Without that definition, every client conversation about results becomes a negotiation rather than a measurement.
FAQ
What is the most important AEO/GEO service component for a client just getting started?
The AI visibility audit, because it tells you where to focus everything else. Without a real baseline, all subsequent optimization is directionally vague. Start with the audit and let it drive the content and citation strategy.
Is schema markup still worth doing if AI engines can read pages without it?
Yes, and more than ever. Schema is not just for AI readability. It signals confidence about what a page is, what question it answers, and who produced it. Pages with comprehensive, correctly implemented schema get cited more consistently across AI engines than equivalent pages without it.
Do I need to do off-site citation building for every client?
For clients in competitive categories, yes. For clients in niches where there is almost no AI-search competition yet, foundational on-site work may produce visible results first. But off-site citation building is the component that compounds over time, and skipping it caps the ceiling of what AI visibility improvement is achievable.
How often should AI visibility tracking happen?
Monthly tracking is the baseline. For clients in fast-moving categories or with active competitors, weekly spot checks on the most commercially important prompts are worth running. The AI search landscape changes often enough that month-old data can miss significant shifts.
What should a monthly AEO/GEO report actually include?
Share of voice trend by engine, citation share by domain and page, prompt coverage count, competitive positioning relative to the two or three closest competitors, AI referral sessions from GA4, and a plain-language summary of what happened and what happens next. Everything else is supporting detail.
How do I know if a competitor is gaining AI visibility faster than my client?
Competitive benchmarking in your tracking tool should show this. Share of voice trends across the same prompt set for multiple brands, tracked over time, make it visible when a competitor is pulling ahead on specific prompt types or within a specific engine.
Summary
- A serious AEO/GEO service covers six components: AI visibility audit, content optimization for answer extraction, schema and structured data implementation, off-site citation and authority building, technical foundations, and ongoing tracking and reporting.
- The AI visibility audit is both the most important starting point and the most powerful sales tool in this service.
- Content optimization means restructuring for clean answer extraction, not just writing more content. Definition blocks, FAQ structures, and deep topical coverage are what get cited.
- Off-site citation building is the most frequently skipped component and one of the most important for sustained GEO results.
- Monthly reporting should cover share of voice, citation share, prompt coverage, competitive benchmarking, and AI referral traffic, delivered in a format that goes straight to a client.
- How the tracking data gets collected matters. Browser-based tracking reflects what real buyers actually see. API-only data frequently does not.
Pierview uses real browser-based tracking across ChatGPT, Perplexity, Gemini, Claude, and AI Overviews to give agencies an accurate share of voice, citation share, and competitive benchmarking data, with white label reports built for monthly client delivery.
