340% More AI Citations in 90 Days: The CITABLE AEO Methodology

Updated December 10, 2025

When your CEO asks "What's our AI search strategy?" in the next board meeting, showing keyword rankings won't answer the question. Your organic MQLs are declining quarter over quarter despite maintaining strong Google positions, and you need a framework built for how buyers actually research today.

Your SEO strategy isn't failing. You've simply optimized for the wrong retrieval system. B2B buyers increasingly use AI platforms for vendor research, and these AI-referred visitors demonstrate significantly higher conversion rates than traditional search traffic.

We'll show you the exact methodology we use at Discovered Labs to engineer content for AI citation. We call it the CITABLE Framework, a systematic approach that helps B2B companies increase their visibility when prospects ask AI platforms for vendor recommendations.

Why traditional SEO fails in AI search

The mechanics of how your prospects discover vendors have fundamentally changed, and your optimization strategy must change with them. Here's why your current SEO approach leaves you invisible when buyers ask AI for recommendations.

Traditional SEO operates on indexing and ranking. Google crawls your content, indexes it based on keywords and backlinks, and then ranks pages in response to queries. The goal is to appear on page one of search results where users can click through to evaluate your offering.

AI search operates on retrieval and synthesis. Large Language Models like ChatGPT, Claude, and Perplexity use Retrieval-Augmented Generation to pull relevant information from vast datasets and synthesize conversational answers. Instead of showing a ranked list of links, these systems generate a single answer that may cite zero, one, or several sources. Your content isn't competing to be the top-ranked result. It's competing to be the retrieved, understood, and cited source within an AI-generated response.

Think of it this way: SEO organized the library so Google could find your book. AEO writes your book so AI can understand it, verify it, and confidently recommend it to every prospect who asks.

The implications for B2B marketing are significant. Gartner predicts search engine volume will drop 25% by 2026 as users shift to AI assistants. Marketing leaders who invested heavily in traditional SEO are watching organic MQLs decline quarter over quarter despite maintaining or improving their Google rankings.

The structural difference demands a structural solution. You cannot simply add "AI optimization" to your existing content calendar and expect results.

The CITABLE framework explained

We built the CITABLE Framework as our proprietary methodology for engineering content that AI systems retrieve, verify, and cite with confidence. Each letter represents a specific optimization phase we use to align your content with how LLMs process information.

This isn't creative writing. It's a repeatable engineering process built on hundreds of tests across multiple AI platforms and industries.

The seven phases:

1. Clear entity & structure
2. Intent architecture
3. Third-party validation
4. Answer grounding
5. Block-structured for RAG
6. Latest & consistent
7. Entity graph & schema

Let's break down each phase.

C: Clear entity & structure

AI models need to understand exactly who you are and what you do within the first few sentences of any page. We call this entity clarity.

We start every major content piece with a BLUF opening (Bottom Line Up Front). State your company name, category, and primary value proposition in the first 50 words. For a project management SaaS: "Acme Project Manager is a cloud-based task management platform for distributed technology teams with 50-500 employees. We centralize project workflows, real-time collaboration, and milestone tracking."

This explicit structure tells the LLM exactly what entity you are. Traditional SEO content buries this information or assumes the reader will infer it. LLMs don't infer well.

We implement this through careful content architecture:

• Every service page opens with a clear entity statement
• Product descriptions start with explicit category definitions
• Case studies include entity context in the opening paragraph
• Schema markup (Organization, Product, Service) reinforces these definitions at the code level

I: Intent architecture

We map 50-100 buyer-intent queries for your category using our 11-step content optimization playbook. For each core topic, we identify the primary question and 3-5 related questions.

If the main query is "What is the best project management software for remote teams?" the adjacent questions might be:

• How much does project management software cost?
• What integrations does project management software need?
• How long does project management software take to implement?

By answering all of these in one comprehensive resource, we create what we call "intent clusters." These clusters increase the probability that an LLM will retrieve your content because you've covered the full scope of what a user wants to know, not just a narrow slice.

T: Third-party validation

AI models trust external sources more than your own website. A statement on your blog carries less weight than a user review on G2, a Reddit discussion, or a mention in a tech publication.

LLMs prioritize consensus and verifiability, which is why third-party validation is critical for AEO.

We build this validation systematically across three channels:

• Reddit presence: We use aged, high-karma accounts to participate authentically in relevant subreddits
• Review generation: We run campaigns on G2 and Capterra to build user-generated feedback consensus
• PR and mentions: We secure coverage in industry blogs and publications through targeted thought leadership

The goal is information consistency across the web. If your pricing, your key features, and your value proposition are mentioned accurately and repeatedly on high-authority third-party sites, AI models interpret this as a strong trust signal.

A: Answer grounding

Vague claims get ignored by AI systems. Specific, verifiable facts get cited. Answer grounding means anchoring every claim to a verifiable data point.

Instead of saying "Our platform improves team productivity," say "Teams using our platform report a 34% reduction in project completion time, based on a survey of 200 customers conducted in Q3 2025." The specificity makes the claim verifiable and less prone to being ignored or misrepresented by an AI model.

We apply this rigorously:

• Include specific metrics, dates, sample sizes, and sources
• Link to original research when citing industry statistics
• Add customer quotes with attribution
• Timestamp case studies and updates

This creates a factual density that LLMs favor when selecting sources to cite. Most LLMs are trained on data up to a specific cutoff date. By providing explicit, recent, and verifiable facts with timestamps, you increase the likelihood that retrieval mechanisms surface your content even if it wasn't in the original training data.

B: Block-structured for RAG

Retrieval-Augmented Generation systems retrieve passages of 200-400 words, not entire pages. We structure content in discrete, self-contained blocks that can stand alone.

Each section of your content should function as a mini-answer. Use clear H2 and H3 headings that describe exactly what the section addresses. Write in 200-400 word sections that fully answer one specific aspect of the broader topic.

We achieve this through strict formatting:

• 5-7 distinct sections with descriptive headings per article
• Bullet points for any list of 3+ related items
• FAQ schema on pages answering common questions
• No walls of text - 200-400 word paragraph maximum
• Tables for comparisons, prices, or feature lists

This structural clarity makes retrieval exponentially more reliable.

L: Latest & consistent

We timestamp every article, case study, and product page to signal freshness to AI models. We include "Updated [Date]" language prominently. When your pricing changes, we update not just your website but also your third-party platform mentions to maintain consistency.

This is particularly important for B2B SaaS, where products evolve rapidly. Conflicting information across sources is a red flag for LLMs. If your website says you have 15 integrations but your G2 profile says 12, an AI model may skip citing you entirely because it cannot determine which source is accurate.

Consistency also applies to entity information:

• Company description
• Founder names
• Headquarters location
• Founding year
• Employee count

All of this should be identical across every platform. Small inconsistencies that a human might overlook can cause an AI model to treat your information as less authoritative.

E: Entity graph & schema

We implement schema markup across your site:

• Organization schema on homepage and about page
• Product schema on product pages
• Review schema on testimonial pages
• FAQ schema on support content

This markup provides a machine-readable map that LLMs process efficiently.

In the content itself, we make relationships explicit through language. Instead of saying "We help teams manage projects," we say "Acme Project Manager (the product) is built by Acme Corporation (the company) for distributed software development teams (the customer) who need real-time collaboration tools (the use case)."

We also use internal linking strategically to reinforce entity relationships. Every product page links to relevant case studies, feature pages, and integration documentation. This creates a knowledge graph that both human users and AI retrieval systems can navigate effectively.

Case study: 7x growth in AI-referred trials

A mid-market B2B SaaS company came to us with a problem that's becoming increasingly common. They ranked well on Google for their core category keywords but never appeared when prospects asked AI platforms like ChatGPT or Perplexity for vendor recommendations.

Their competitors did. Consistently. This measurable citation gap was costing them pipeline.

The execution

We implemented the full CITABLE Framework over several weeks:

1. Entity clarity: Restructured core product pages with BLUF openings and clear entity definitions
2. Content production: Published 45 new articles engineered for LLM retrieval with block structure and verifiable data
3. Third-party validation: Secured 20+ authentic mentions on high-authority Reddit communities using our proprietary aged account infrastructure
4. Technical implementation: Added comprehensive schema markup across their site
5. Review generation: Increased G2 reviews from 40 to 120 to build consensus signals

The results

AI-referred trials grew from 500 to over 3,500 per month within seven weeks. The client now appears in ChatGPT recommendations alongside competitors with 10x their marketing budget. They reversed a two-quarter decline in organic MQLs and built a repeatable process for maintaining AI visibility as new platforms emerge.

How to measure AEO success

You cannot measure AEO success with traditional SEO metrics like keyword rankings and domain authority. You need a new framework built around AI-specific outcomes.

1. Citation rate is the primary metric. This measures the percentage of relevant buyer-intent queries where your brand is mentioned in AI-generated answers. We track this across ChatGPT, Claude, Perplexity, Google AI Overviews, and Microsoft Copilot. A citation rate of 15-20% is solid for a new AEO program, while 30-40% is excellent.

2. Share of voice measures your citation frequency relative to competitors. If you're cited in 40% of queries while competitors average 25%, you have a strong competitive position. Share of voice is particularly important for executive reporting because it shows relative market positioning for board presentations.

3. Sentiment in citations tracks how AI models describe your brand when they do cite you. Are you mentioned as "best for" a specific use case? Do the AI-generated descriptions align with your positioning? Positive, accurate sentiment reinforces your brand even when prospects don't immediately convert.

4. AI-referred MQLs and pipeline are the bottom-line metrics. We track visitors arriving from AI platforms using UTM parameters and custom tracking in HubSpot and Salesforce. We measure their conversion rates, sales cycle length, and close rates compared to traditional search traffic.

We provide weekly visibility reports that track all of these metrics, showing both absolute progress and competitive benchmarks.

AEO agency vs. SEO agency comparison

If you're evaluating whether your current SEO agency can execute AEO, this comparison shows the critical differences you need to assess.

The most critical difference is methodology. Many traditional SEO agencies have added "AEO" to their service pages without fundamentally changing their content production process. They're still writing keyword-focused blog posts designed for Google's algorithm, then hoping those posts also work for AI citation.

A specialized AEO agency builds content from the ground up for LLM retrieval. The format, structure, entity clarity, validation signals, and technical implementation are all engineered specifically for how AI models retrieve and cite information.

The window to establish AI visibility is closing

The opportunity to establish your brand as a trusted AI source is time-sensitive. The companies that implement systematic AEO now will shape how AI models understand their categories. When your board asks about your AI visibility strategy, you'll have data showing competitive positioning, not excuses about algorithm changes.

LLMs are trained on large datasets up to a specific knowledge cutoff date. Information published after that date may not be part of the model's base knowledge, though retrieval mechanisms can surface recent content. Brands that establish strong, consistent, well-structured presence early are more likely to be integrated into the foundational knowledge of these models.

If your brand is invisible when prospects ask AI for vendor recommendations, you're not losing to better products. You're losing to better-structured information. The cost isn't just missed opportunities - it's watching your organic pipeline decline while competitors capture AI-referred leads that convert at significantly higher rates.

Ready to see where you stand? You can request your free AI visibility audit to see side-by-side screenshots of where your company and your top 3 competitors appear when prospects ask ChatGPT, Claude, and Perplexity for recommendations in your category, or download our Citable framework implementation checklist to audit your current content and identify quick wins.

Frequently asked questions

What's the difference between AEO and GEO?
Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) refer to the same practice of optimizing content for AI-powered search platforms. Both focus on increasing citation rates in LLM-generated answers rather than traditional keyword rankings.

How long does it take to see results from CITABLE implementation?
You'll typically see initial citations within 1-2 weeks for high-priority queries, and 20-30% citation rates within 1-3 months with consistent execution. Measurable pipeline impact generally requires 3-4 months as AI-referred leads progress through your sales cycle.

Can traditional SEO and AEO strategies coexist?
Yes, and they should coexist. AEO complements SEO by capturing the growing segment of buyers who use AI for research, and the entity clarity required for AEO often improves your traditional SEO performance as well.

What's the typical investment for implementing the CITABLE Framework?
Managed AEO services typically range from $5,000 to $20,000 per month depending on content volume and competitive intensity. Our AEO pricing starts at €5,495/month for 20+ articles plus audits and Reddit marketing, with month-to-month terms.

Do I need to rebuild my entire content library for AEO?
No, we prioritize high-impact pages first: core product pages, category definition content, and high-intent buyer query pages. A phased approach over 90 days is more effective than trying to overhaul everything at once.

What ROI can I expect from CITABLE implementation?
Our clients typically see measurable increases in AI-referred traffic within the first quarter, with these visitors converting at significantly higher rates than traditional search traffic. Cost per AI-referred MQL averages 50% lower than traditional content marketing CPL after the first quarter.

Key terms glossary

Answer Engine Optimization (AEO): The practice of structuring content so AI-powered search platforms can retrieve, understand, and cite it accurately when answering user queries. Our complete guide to answer engine optimization covers the fundamentals.

Retrieval-Augmented Generation (RAG): A technical approach where AI models retrieve relevant information from external sources and use it to generate more accurate responses. RAG is the core mechanism behind ChatGPT Search, Perplexity, and similar platforms.

Citation Rate: The percentage of relevant buyer-intent queries where your brand is mentioned in AI-generated answers. A citation rate of 30-40% indicates strong AI visibility in your category.

Share of Voice: Your brand's citation frequency compared to competitors in AI-generated answers. If you're cited in 40% of queries while competitors average 25%, you have a dominant AI share of voice.

Entity Clarity: The explicit definition of who your company is, what you do, and how you relate to other entities. AI models require explicit entity definitions to cite sources accurately.

Intent Clustering: Answering a primary query and related adjacent questions in one comprehensive content piece. Intent clusters increase the probability that AI models will retrieve and cite your content.

Knowledge Cutoff: The date beyond which an AI model's training data does not extend. Most LLMs have knowledge cutoffs, though retrieval mechanisms can surface recent information when properly structured.