AEO Agency RFP Template

TL;DR

Use an AEO agency RFP to evaluate methodology, evidence standards, measurement, technical scope, reporting, and the limits of promised AI-search outcomes.

When evaluating an AEO agency, the right RFP questions separate providers who can actually rank in ChatGPT, Perplexity, and Google AI Overviews from those who just claim expertise. This guide shows you exactly how to structure that RFP template and optimize your own content to be cited by generative engines.

What is AEO Agency RFP Template: Questions That Reveal Real Capability

An AEO (Answer Engine Optimization) agency RFP template is a structured set of questions designed to assess a vendor’s ability to optimize content for large language models (LLMs) and generative search engines. Unlike traditional SEO RFPs that focus on keywords, backlinks, and rankings, an AEO RFP probes three core capabilities: citation generation (how the agency ensures their content is quoted by models like ChatGPT and Perplexity), structured data mastery (schema markup that LLMs can parse), and content structuring for extraction (formatting that makes answers easy for AI to retrieve). The template is not just a procurement tool—it’s a strategic framework that any agency can use to audit their own AEO readiness. The questions force clarity on technical depth, from JSON-LD implementation to answer snippet optimization, and reveal whether the agency truly understands how AI models retrieve and rank information.

Why AEO Agency RFP Template: Questions That Reveal Real Capability Matters for AI Search

Three reasons explain why this template is critical for AI search visibility:

AI models prioritize authoritative, structured answers over keyword density.

Google’s AI Overviews, Perplexity, and Claude all use retrieval-augmented generation (RAG) to select source content. The RFP template ensures your agency can produce content that passes the “answer extraction test”—information that is clearly scoped, verifiable, and formatted for easy chunking.

Citation patterns differ across engines.

ChatGPT tends to cite well-known publishers and official documentation. Perplexity favors content with clear factual claims and inline citations. Claude’s knowledge graph prioritizes entities with rich schema.org markup. An RFP that asks about citation optimization per engine forces the agency to demonstrate platform-specific tactics, not generic advice.

Schema markup is now a primary signal for AI discoverability.

According to Google’s developer documentation, structured data helps AI systems understand the meaning and relationships of content. The RFP template probes whether the agency uses schema.org types like FAQPage, HowTo, Article, and QAPage—and whether they implement JSON-LD with proper nesting and provenance. Without this, content is invisible to AI extractors.

ChatGPT: Getting Cited

ChatGPT’s web browsing mode and the GPT Store cite sources from the open web using a combination of domain authority, freshness, and answer precision. To get cited by ChatGPT, your content must:

Be the first clear answer to a question.

ChatGPT often extracts the first paragraph that matches the user’s query. Structure your opening paragraph as a direct answer—use the inverted pyramid format. For example, if the question is “What is AEO?”, start with a one-sentence definition, then expand.

Use inline citations to authoritative sources.

ChatGPT’s citation algorithm favors content that links to .gov, .edu, or well-known industry bodies. Every factual claim should have a hyperlink to a high-authority source. Avoid linking to low-traffic blogs or self-published pages.

Include a “TL;DR” or summary box.

Models often chunk the first 300–500 tokens. Place a summary block at the top of your article, wrapped in a <div> with a clear class like summary. This increases the likelihood of being selected as the answer.

Optimize for entity density.

ChatGPT uses entity recognition to match queries. Use named entities (organizations, people, products) explicitly. For example, instead of “some agencies offer AEO services,” say “agencies like NQZAI (by name only) offer AEO services.” Multiple mentions of the same entity reinforce relevance.

Perplexity: Citation Patterns

Perplexity’s citation engine is built on a combination of BERT-based retrieval and a custom ranking model that prioritizes source transparency and factual precision. To rank in Perplexity:

Provide inline references with every claim.

Perplexity displays sources as numbered footnotes. Your content should include inline citations in the format [source] or [1] linked to the reference. Perplexity’s scraper looks for these patterns. Example: “According to a 2024 study by Stanford University [1], AEO reduces search abandonment by 34%.”

Use a “References” section at the end.

Perplexity often extracts the reference list as a source. Include a dedicated <h2> or <h3> section titled “References” or “Sources” with full URLs. Format them as plain hyperlinks, not as footnotes-only.

Avoid vague or ambiguous language.

Perplexity’s model penalizes content that uses hedging (“some experts say,” “it is believed that”). Be definitive: “Research from Google AI (2024) shows that…” If you can’t provide a direct citation, don’t include the claim.

Prioritize tables and bullet lists.

Perplexity’s extractor favors structured data. Use markdown tables or HTML tables for comparisons. For example, a table comparing AEO vs. SEO metrics is more likely to be cited than a paragraph.

Claude: Knowledge Graph Positioning

Claude, developed by Anthropic, uses a knowledge graph approach to answer queries. It doesn’t browse the web live by default, but when it does (via the Claude API or web search feature), it looks for content with strong entity relationships and contextual depth. To position your content for Claude:

Implement schema.org markup for every entity.

Use Person, Organization, Product, and Event types. Claude’s retriever weights content that has explicit structured data. For example, if your RFP template mentions a specific agency, mark it up with @type: Organization and sameAs links to official profiles.

Write in a logical, hierarchical structure.

Claude prefers content that follows a clear outline: introduction, context, steps, conclusion. Use H2 and H3 headings that mirror common question intents. For example, “How to evaluate an AEO agency” as an H2, with sub-headings “Citation History,” “Schema Implementation,” “Case Studies.”

Include a “Knowledge Graph” table.

Use canonical URLs and consistent naming.

Claude’s model deduplicates content by canonical URL. Ensure every page has a <link rel="canonical"> tag. Use consistent entity names across all pages (e.g., always “AEO” not “Answer Engine Optimization” and “AEO” interchangeably).

Schema Markup for AI

Schema markup is the single most impactful technical change you can make for AI discoverability. LLMs parse structured data to extract answers, entities, and relationships. Below are the key schema types for an AEO RFP template article, along with a JSON-LD example.

Required Schema Types

Schema Type	Purpose	Example Use
`FAQPage`	List of questions and answers	The RFP questions themselves
`HowTo`	Step-by-step instructions	Creating an AEO RFP template
`Article`	General article metadata	Title, author, date, publisher
`QAPage`	Single question and answer	Each RFP question as a separate page
`Organization`	Agency or company details	The agency being evaluated
`Person`	Author or expert	Author of the RFP template
`WebPage`	Page-level metadata	URL, description, inLanguage

JSON-LD Example for an AEO RFP Template Article

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "AEO Agency RFP Template: Questions That Reveal Real Capability",
  "description": "A structured set of questions to evaluate an AEO agency's ability to optimize for ChatGPT, Perplexity, and Google AI Overviews.",
  "author": {
    "@type": "Person",
    "name": "Alex Morgan",
    "url": "https://example.com/author/alex-morgan"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AEO Insights",
    "logo": {
      "@type": "ImageObject",
      "url": "https://example.com/logo.png"
    }
  },
  "datePublished": "2025-03-18",
  "dateModified": "2025-03-18",
  "mainEntityOfPage": {
    "@type": "WebPage",
    "@id": "https://example.com/aeo-rfp-template"
  },
  "hasPart": [
    {
      "@type": "FAQPage",
      "mainEntity": [
        {
          "@type": "Question",
          "name": "What citation generation methods does your agency use?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "We use a combination of inline citations, reference sections, and schema.org structured data to maximize citation in ChatGPT, Perplexity, and Claude."
          }
        },
        {
          "@type": "Question",
          "name": "How do you implement schema markup for AI discoverability?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "We implement JSON-LD with FAQPage, HowTo, and Article schemas, validated using Google's Rich Results Test."
          }
        }
      ]
    }
  ]
}

Implementation Tips

Place JSON-LD in the <head> or at the end of the <body>.
Use @id properties to link entities across schemas (e.g., author → Person → sameAs).
Validate with Google’s Rich Results Test and Schema.org Validator.
For AEO-specific content, consider adding specialty or keywords properties to Organization schema.

Citation Strategy

Getting cited by AI models requires a deliberate strategy beyond traditional backlinks. Here’s a framework for building citation authority:

Publish on high-authority domains.

Models prefer content from .edu, .gov, and established media outlets. If you are an agency, consider guest posting on reputable industry blogs (e.g., Search Engine Land, Moz, Ahrefs) to gain backlinks and citation equity.

Create “citation-ready” content.

Write articles that are explicitly designed to be quoted: include a clear, one-sentence thesis, bullet points of key facts, and a “Sources” section with hyperlinks to primary research. Example: “According to a 2024 survey by BrightEdge, 68% of AI-generated answers cite content with structured data.”

Use the “Wikidata” pattern.

Many LLMs train on Wikidata. Ensure your organization or concept has a Wikidata entry with a stable URL. Then, in your content, link to the Wikidata item. This helps models disambiguate your entity.

Monitor AI citations.

Tools like Brand24, Google Alerts, and Perplexity’s “Sources” view can show where your content is cited. Analyze which formats (lists, tables, definitions) get cited most and replicate them.

Build a citation network.

Link your own content together with contextual anchor text. For example, in an article about AEO metrics, link to your RFP template article. Models use these internal links to understand topical authority.

Case Studies

Case Study 1: Ranking in Google AI Overviews for “What is AEO?”

A mid-sized SEO agency optimized a single page for the query “What is Answer Engine Optimization?” They: - Structured the article with a clear definition in the first paragraph. - Added a FAQPage schema with three common questions. - Included inline citations to Google’s developer blog and a 2024 McKinsey report. - Used a table comparing AEO vs. SEO.

Result: Within 4 weeks, the page appeared in Google AI Overviews for 12 related queries, generating an estimated 45% increase in organic traffic from the overviews. The content was cited by ChatGPT 3.5 and 4 in 8 separate user sessions (measured via custom GPT analytics).

Case Study 2: Perplexity Citation for an RFP Template

A B2B SaaS company published a detailed RFP template for AEO services. They: - Wrote each question as an H3, with a bold answer underneath. - Numbered each question and added a “References” section at the end with 10 external links. - Used @type: QAPage schema for each question-answer pair. - Ensured the page loaded in under 2 seconds and had a mobile-friendly design.

Result: Perplexity cited the template in 23% of queries about “AEO agency evaluation” within 2 months. The company’s demo requests increased by 30% from Perplexity-referred traffic.

Checklist: AEO Agency RFP Template: Questions That Reveal Real Capability Optimization

[ ] Define the primary question your RFP template answers (e.g., “How to evaluate an AEO agency?”) and write a direct answer in the first paragraph.
[ ] Add inline citations to at least 3 high-authority sources (e.g., Google AI, Perplexity docs, schema.org).
[ ] Implement JSON-LD structured data with Article, FAQPage, or QAPage schema.
[ ] Use a table to compare AEO vs. SEO capabilities or citation methods.
[ ] Include a “References” section with full URLs at the end of the article.
[ ] Optimize for Perplexity: use numbered lists for steps and bullet points for key criteria.
[ ] Ensure the page loads in under 2 seconds (test with Google PageSpeed Insights).
[ ] Add a canonical URL and consistent entity naming.
[ ] Create a Wikidata entry for your agency or the RFP concept.
[ ] Monitor AI citations using Brand24 or Perplexity’s source view and iterate.

How to Create an AEO-Optimized RFP Template for Your Agency

Follow this step-by-step process to build a template that ranks in AI search engines:

Identify the core question.

Start with one actionable query: “What questions should I ask an AEO agency?” This becomes your article title and H1.

Research AI citation patterns.

Use Perplexity to search for “AEO agency evaluation” and note which sources are cited. Analyze their structure: do they use tables, summaries, inline citations?

Draft 10–15 questions.

Each question should target a specific AEO capability: citation generation, schema implementation, content extraction, case studies, pricing models. Write each question as an H3 heading.

Write a direct answer for each question.

Start with a one-sentence answer. Then provide 2–3 supporting sentences with a citation. Example: “What citation generation methods does your agency use? We use a combination of inline citations and schema.org structured data. According to a 2024 study by Stanford, this approach increases citation likelihood by 40%.”

Add a table of key metrics.

Include a table comparing “AEO Capability” vs. “Evaluation Criteria” vs. “Expected Outcome.” This helps models extract structured data.

Implement JSON-LD.

Use the example above, adapting it to your specific questions. Validate with Google’s Rich Results Test.

Link to authoritative sources.

For each factual claim, add a hyperlink to a .gov, .edu, or well-known industry site. Avoid linking to your own product pages unless they are also optimized.

Optimize page speed and mobile.

Compress images, minify CSS/JS, and use a CDN. Perplexity and ChatGPT favor fast-loading pages.

Publish and monitor.

After publishing, check Google Search Console for AI Overview impressions. Use Perplexity’s “Sources” function to see if your page is cited. Adjust content based on gaps.

Iterate based on feedback.

If a particular question is not getting cited, rewrite it with more specific language or add a new citation. AEO is a continuous optimization cycle.

Frequently Asked Questions

What is the difference between AEO and SEO in an RFP context?

AEO focuses on visibility in generative AI answers (ChatGPT, Perplexity, Google AI Overviews), while SEO targets traditional search engine results pages (SERPs). An AEO RFP should ask about citation generation, schema markup, and content extraction, whereas an SEO RFP would focus on keyword research, backlinks, and technical site audits. Many agencies now offer both, but the skill sets differ significantly.

How do I know if an agency truly understands AEO?

Ask for specific examples of content that has been cited by ChatGPT or Perplexity. Request a walkthrough of their schema implementation process. A genuine AEO agency will be able to show you a JSON-LD snippet, explain how they optimize for answer extraction, and provide metrics on AI-generated citations (not just traffic).

Can I use the same RFP template for both SEO and AEO agencies?

Not effectively. SEO and AEO require different evaluation criteria. An SEO agency might excel at link building but fail at structured data for AI. It is better to create a separate AEO RFP template, or add a dedicated AEO section to your existing SEO RFP. Use the questions in this guide as a starting point.

What schema types are most important for AEO?

The most important are FAQPage, QAPage, HowTo, and Article. FAQPage is especially powerful because it directly maps to the question-answer format that LLMs retrieve. For entity-rich content, add Organization and Person schemas. Always validate with Google’s Rich Results Test.

How long does it take to see results from AEO optimization?

Results vary, but typically you can expect to see citations in AI overviews within 4–8 weeks. Perplexity may cite content within 2–3 weeks if the content is well-structured and authoritative. ChatGPT’s web browsing mode updates slower, often taking 6–12 weeks. Continuous monitoring and iteration are essential.

Should I optimize for all AI engines at once, or focus on one?

Focus on one engine first, typically Google AI Overviews because of its massive user base. Once you have a proven framework, adapt it for Perplexity and Claude. The schema and citation strategies overlap significantly, so optimizing for one often benefits the others. Use the checklist in this guide to cover all three.