GEO Case Study: How We Got a Client Cited in 87% of AI Search Results

When a prospect asks ChatGPT, Perplexity, or Google’s AI Overviews for a recommendation in your industry, is your brand mentioned? For most businesses, the answer is no — and they don’t even know they’re invisible in what’s increasingly becoming the primary search interface for millions of users.

This is the challenge that Generative Engine Optimization (GEO) addresses. And in this case study, we’ll walk through exactly how we took a mid-sized B2B software client from zero AI mentions to appearing in 87% of AI-generated responses to queries in their target category.

Background: The Client and the Problem

Our client — a project management software company we’ll call “ProjectFlow” (details anonymized for client confidentiality) — had strong traditional SEO performance. They ranked on page 1 for dozens of target keywords, had 40,000 monthly organic visitors, and a robust content library of 200+ articles.

Despite this, when their sales team began probing how prospects found them, a troubling pattern emerged: fewer and fewer were coming through Google organic search. Post-purchase surveys showed “I searched on ChatGPT” appearing in 18% of responses by Q2 2025 — up from near zero in 2023.

When we audited AI search visibility, the results were stark: ProjectFlow was mentioned in approximately 12% of AI-generated responses to queries like “best project management software for small teams,” “what project management tool should I use,” and similar intent queries. Competitors with inferior traditional SEO were being cited 3-4x more frequently.

Understanding Why AI Systems Cite What They Cite

Before building a strategy, we needed to understand the mechanics of AI citation. Through extensive testing across ChatGPT, Perplexity, Claude, and Google AI Overviews, we identified the primary signals that influence whether a brand appears in AI-generated responses:

1. Authority in Training Data

LLMs are trained on large corpora of web content. Brands that appear frequently in high-quality, widely-referenced sources — major publications, industry journals, respected blogs — are more likely to be embedded in the model’s “knowledge” of a category. This is why being cited in Forbes, TechCrunch, G2, Capterra, and industry-specific publications matters more than it used to.

2. Retrieval-Augmented Generation (RAG) Sources

Many AI systems — especially Perplexity and the “with search” modes of ChatGPT and Claude — use real-time web search to retrieve current information. This means ranking in traditional search still matters, but specifically for the queries and formats that AI systems tend to retrieve from. AI retrieval strongly favors:

• Comparison and listicle articles (“best X for Y”)
• Review aggregator sites (G2, Capterra, Trustpilot, Product Hunt)
• Reddit discussions and forum content
• Recent news and press coverage
• Structured data and FAQ content

3. Schema Markup and Structured Data

AI systems parse structured content more efficiently than flowing prose. Pages with proper schema markup — particularly Organization schema, Product schema, FAQ schema, and Review schema — are more readily parsed and cited by AI systems.

4. Brand Mention Patterns

The context in which a brand is mentioned matters. Appearing alongside industry terms, being mentioned in comparison with established competitors, and being cited as an example in educational content all reinforce the AI’s understanding of what a brand is and what category it belongs to.

The GEO Strategy We Implemented

Phase 1: Authority Building (Months 1-2)

Target publication placement: We identified 25 high-authority publications that AI systems regularly cite when answering project management software queries. Through a combination of PR outreach, contributed articles, and expert commentary campaigns, we secured ProjectFlow mentions in 14 of these publications within 60 days.

Key placements included: a feature in Inc.com’s “Best Project Management Tools” roundup, a contributed article in Harvard Business Review’s digital strategy section, expert quotes in Forbes coverage of remote work productivity tools, and a case study in the Project Management Institute’s quarterly journal.

Review platform dominance: We identified that G2, Capterra, and GetApp were the most frequently cited sources when AI systems answered software recommendation queries. ProjectFlow had 43 G2 reviews. We launched a systematic review generation campaign, reaching 287 reviews within 60 days, with an average rating of 4.7/5. We also ensured their profiles were complete, keyword-rich, and included detailed feature comparisons.

Reddit community strategy: Reddit is heavily indexed by AI systems for product recommendations. We identified 8 subreddits where project management software discussions occurred regularly (r/projectmanagement, r/entrepreneur, r/smallbusiness, etc.). We trained a team member as a genuine community contributor — answering questions, providing real value, and naturally mentioning ProjectFlow where relevant and appropriate. Within 60 days, ProjectFlow was being mentioned organically by other community members in response to recommendation requests.

Phase 2: Content Restructuring (Month 2-3)

AI-optimized content formats: We audited ProjectFlow’s 200-article content library and identified that only 31% was structured in formats that AI systems prefer to cite. We restructured high-value articles to include:

• Clear FAQ sections with schema markup
• Explicit comparison tables (AI systems love structured comparisons)
• Direct, declarative statements of value (rather than marketing language)
• Statistics and data points with clear attribution
• Step-by-step numbered processes

“Best answer” content creation: We identified the 50 most common AI queries in the project management space and created or updated content specifically designed to be the authoritative answer to each. These weren’t keyword-stuffed SEO articles — they were genuinely comprehensive responses to the query, structured to be citeable.

Schema markup implementation: Added Organization schema, SoftwareApplication schema, FAQ schema on all key pages, and AggregateRating schema (pulling from review data) site-wide. This structured data helps AI systems understand exactly what ProjectFlow is and what it does.

Phase 3: Competitive Positioning in AI Knowledge (Month 3-4)

Comparison content strategy: One of the strongest signals that gets a brand cited in AI responses is appearing in comparison content. We created a comprehensive comparison hub — “ProjectFlow vs [Competitor]” pages for the 12 most common competitors in the space. These pages were structured to be objective, data-driven, and genuinely useful. AI systems pulled from these pages heavily when answering “what’s the difference between X and Y” queries.

Industry data ownership: We commissioned two original research studies — a “State of Project Management 2025” survey (n=500 respondents) and a productivity analysis study. Original data that other publications cite creates a powerful citation network: when 20+ publications reference your data, AI systems learn to associate your brand with authority in the space.

Wikipedia and knowledge graph presence: ProjectFlow had no Wikipedia page. For companies of sufficient size and significance, Wikipedia presence significantly influences AI knowledge graph associations. We worked with a qualified Wikipedia contributor (following all Wikipedia guidelines) to create a neutral, properly cited company article. The Google Knowledge Panel followed within 6 weeks.

Results: 90-Day Measurement

AI Citation Rate

We measured AI citation rates using a consistent methodology: 50 standardized queries per week across ChatGPT (GPT-4o), Perplexity, Claude, and Google AI Overviews. Queries spanned informational (“what is the best project management software”), comparison (“project management software vs task managers”), and recommendation (“what should a 20-person startup use for project management”) intents.

• Baseline (Month 0): 12% citation rate (6/50 queries resulted in ProjectFlow mention)
• Month 1: 28% citation rate
• Month 2: 54% citation rate
• Month 3: 71% citation rate
• Month 4: 87% citation rate

Business Impact

• Organic trial signups increased 34% over the period (some attributable to GEO, some to continued traditional SEO)
• Post-purchase survey “found us via AI search” responses increased from 18% to 41%
• Brand search volume (tracked via GSC) increased 67% — indicating increased brand awareness
• Perplexity-referred traffic (tracked via analytics) increased 890%
• G2 profile became a top-5 traffic source to their website for the first time

Key Lessons for GEO Practitioners

GEO Is Earned, Not Bought

Unlike paid search, you cannot directly buy AI citations. The signals that drive AI visibility — genuine reviews, earned media mentions, community discussions, cited research — require real work and often real time. There are no shortcuts that work reliably.

Quality Reviews Are Irreplaceable

The single highest-impact activity we executed was the G2 review campaign. AI systems treat review aggregators as highly authoritative sources for product recommendations. A company with 300 genuine 4.7-star reviews will be cited far more frequently than a competitor with 20 reviews and slightly better product features.

Structured Data Matters More Than You Think

Many companies still treat schema markup as optional. For GEO, it’s foundational. AI systems parse schema to understand entity relationships, product categories, and comparative attributes. Without it, you’re making AI systems work harder to understand you — and they’ll often default to better-structured competitors.

AI Visibility Compounds

The 12% → 87% journey wasn’t linear because of a single tactic. It compounded: each new authoritative mention made the next one easier, each structured piece of content built on the previous one, and each review generated made the brand’s rating more statistically authoritative. GEO rewards sustained, multi-channel investment.

Conclusion

The brands that will dominate the next 5 years of search won’t just rank on Google — they’ll be the names that AI assistants consistently recommend when users ask for guidance. That position is buildable, measurable, and reproducible with the right strategy.

GEO isn’t replacing SEO. It’s an expansion of the discipline into the channels where search behavior is increasingly happening. The companies that understand this now, and invest accordingly, will have compounding advantages that become very difficult to overcome later.

The playbook exists. The results are measurable. The only question is whether you’ll implement it before your competitors do.