When someone asks ChatGPT “what’s the best CRM for small businesses,” your content might be part of the answer—or it might not exist in that conversation at all.
A Nashville B2B software company discovered this accidentally. Their marketing team noticed support tickets mentioning “ChatGPT recommended you.” Curious, they started testing queries about their product category across AI platforms: ChatGPT, Perplexity, Claude, Bing’s Copilot. Their technical documentation was being cited heavily. Their marketing pages weren’t mentioned at all.
Same company, different content, completely different AI visibility.
This is the core insight of Generative Engine Optimization: AI systems don’t rank pages—they cite sources when synthesizing answers. Getting cited requires understanding how these systems select what to reference, which differs significantly from how Google ranks search results.
GEO isn’t about Google specifically (that’s covered in AI Overviews guidance). It’s about the broader ecosystem of AI-powered search and assistant tools that increasingly mediate how people find information.
The AI Search Ecosystem
Google AI Overviews get the most attention, but they’re one player in a growing field:
ChatGPT with browsing searches the web and synthesizes answers, citing sources inline. Hundreds of millions of users access it directly.
Perplexity positions itself as an “answer engine,” providing sourced responses to queries. It’s gaining significant traction among researchers and knowledge workers.
Claude (what you might be reading this through) can search and cite sources when answering questions.
Bing Copilot integrates AI responses into Microsoft’s search engine, reaching users through Edge and Windows integration.
Specialized AI tools in various verticals—legal research, medical information, financial analysis—also cite web sources when generating responses.
Each system works somewhat differently, but they share common characteristics:
| Platform | How It Cites | User Base | Content Access |
|---|---|---|---|
| ChatGPT (browsing) | Inline citations with links | Massive, general | Web search at query time |
| Perplexity | Numbered citations, source panel | Growing, research-oriented | Web search, indexed sources |
| Claude | Web search with citations | Growing, professional | Web search when enabled |
| Bing Copilot | Integrated citations | Large via Microsoft | Bing index + real-time |
The combined reach of these platforms represents a significant and growing share of how people seek information. Optimizing only for traditional Google rankings ignores this expanding channel.
How AI Systems Select Sources
AI systems don’t crawl and rank pages like traditional search engines. They retrieve information at query time (or from training data) and decide what to cite based on different criteria.
Understanding these selection factors—to the extent we can observe them—shapes GEO strategy:
Factual clarity and specificity
AI systems synthesizing answers need clear, citable statements. Content that makes specific, factual claims—”The average small business spends $1,200-3,000 monthly on CRM software” rather than “CRM costs vary”—provides extractable material.
The Nashville software company’s technical documentation got cited because it contained specific integration steps, exact API endpoints, and precise configuration requirements. Their marketing pages spoke in generalities that don’t synthesize well.
Source authority signals
AI systems attempt to cite trustworthy sources. The same authority signals that help traditional SEO—domain reputation, E-E-A-T indicators, backlinks from credible sites—appear to influence citation likelihood.
A random blog making bold claims competes poorly against an established industry publication making the same claims. Authority transfers into AI citation selection.
Information uniqueness
Common knowledge available everywhere doesn’t need specific citation. Unique information—original research, proprietary data, distinctive frameworks—requires attribution when AI systems use it.
The Nashville company’s documentation contained unique technical details no one else had. That uniqueness made citation necessary when AI systems referenced their integration capabilities.
Content freshness
For topics that evolve, AI systems favor current sources. Citing a 2019 article about current software pricing creates response quality problems. Systems appear to weight recency for time-sensitive information.
Structural accessibility
Content that’s well-organized with clear headings, direct answers to questions, and logical flow is easier for AI to parse and cite. Dense, unstructured prose buries citable information.
Creating Citation-Worthy Content
GEO isn’t a separate content strategy—it’s an additional lens on content that’s already valuable.
Lead with answers, not setup
AI systems extracting information favor content that states conclusions clearly, especially early in relevant sections.
Instead of:
“There are many factors businesses consider when selecting CRM software. These include pricing, features, integration capabilities, and scalability. After evaluating all options…”
Lead with:
“For small businesses under 50 employees, HubSpot and Zoho typically offer the best value balance. Here’s how the factors break down…”
Both contain the same information. The second is more citable.
Include specific, verifiable claims
“CRM implementation typically takes 2-4 weeks for small businesses with basic requirements, extending to 8-12 weeks for complex integrations with existing systems.”
That sentence can be cited. “Implementation takes some time depending on your needs” cannot.
The Nashville company’s technical docs were full of specifics: “Authentication requires OAuth 2.0 with refresh tokens expiring after 30 days.” AI systems cited these because the specificity made attribution necessary.
Create unique intellectual property
Original research gets cited because AI systems can’t attribute unique data to generic sources:
- Surveys and studies with your own data
- Proprietary frameworks and methodologies
- Case studies with specific, named results
- Expert analysis that goes beyond consensus
A Nashville marketing agency created quarterly benchmarks for Tennessee small business marketing spend. AI systems regularly cite this data because no one else has it.
Structure for extraction
- Use descriptive headings that match how people ask questions
- Include summary statements at section beginnings
- Create bullet points and tables for data-heavy information
- Provide clear definitions for technical terms
This isn’t about “optimizing for AI”—it’s about making content that communicates clearly, which helps human readers and AI systems alike.
Technical Requirements for AI Access
AI systems can only cite content they can access.
Crawl accessibility
Most AI search tools crawl the web at query time or maintain indexes. If your robots.txt blocks their crawlers, you can’t be cited.
Current known AI crawlers include:
- GPTBot (OpenAI/ChatGPT)
- Claude-Web (Anthropic)
- PerplexityBot
- Bingbot (serves Bing and Copilot)
Blocking these crawlers is a legitimate choice if you have content concerns, but it precludes GEO benefits. You can’t have AI citation while blocking AI access.
Content availability
Information hidden behind logins, paywalls, or JavaScript that crawlers can’t execute won’t be accessible. Core content should be available on page load without interaction.
Some platforms have arrangements with specific publishers. Perplexity has partnerships allowing access to paywalled content. These arrangements are inconsistent across platforms.
Structured data
While structured data’s role in AI citation isn’t confirmed, it helps AI systems understand content semantically. Organization, author, and factual claim markup may provide useful signals.
Measuring GEO Success
GEO measurement is harder than traditional SEO because citations happen on third-party platforms you don’t control.
What you can track:
Referral traffic from AI sources
Check analytics for referrers including:
- chat.openai.com
- perplexity.ai
- bing.com/chat
- claude.ai
Traffic from these sources indicates your content is being cited with users clicking through.
The Nashville software company tracked this and found Perplexity driving 340 monthly visits—not huge, but with 12% conversion rate to demo requests, significantly higher than their Google organic average.
Manual citation testing
Periodically test queries in your topic area across AI platforms. Note:
- Whether AI Overviews/responses appear
- Whether you’re cited
- How you’re characterized when cited
- Which competitors get cited instead
This is labor-intensive but provides direct visibility into citation status.
Branded search trends
If AI systems mention your brand in responses, some users will search for you directly afterward. Growing branded search volume that doesn’t correlate with other marketing activities may indicate AI exposure.
Indirect engagement patterns
Users arriving from AI citations may behave differently—often more qualified because they’ve already received context. Monitor:
- Conversion rates from AI referral traffic
- Engagement depth (pages per session, time on site)
- Query patterns in site search
What you can’t easily track:
- Total citation frequency across all AI queries
- Impressions where you’re cited but users don’t click
- How citations influence perception when users don’t visit
Accept measurement limitations. GEO is currently higher-uncertainty than traditional SEO measurement. This doesn’t mean it’s not worth pursuing—it means expectations should be calibrated.
GEO Strategy Development
Step 1: Audit current AI visibility
Test your key topics across AI platforms. For 10-20 important queries:
- Does an AI response appear?
- Are you cited? Are competitors?
- How is information from your content characterized?
This baseline reveals your starting position.
Step 2: Identify high-value citation opportunities
Not all queries warrant GEO investment. Prioritize:
- Queries with clear AI response patterns
- Topics where you have unique expertise or data
- Queries that align with business goals (not just traffic)
The Nashville software company found AI systems heavily served “how to integrate [product category]” queries but rarely their “what is [product category]” queries. They focused GEO efforts on integration content where they had technical depth.
Step 3: Create or improve citation-worthy content
For priority topics:
- Ensure content includes specific, citable claims
- Add unique data or analysis where possible
- Structure for clear extraction
- Update stale information
Step 4: Build authority signals
Citation likelihood correlates with perceived authority. Traditional authority-building efforts—quality backlinks, industry recognition, expert authorship—support GEO as well as traditional SEO.
Step 5: Monitor and iterate
Quarterly reassessment:
- Retest citation status for key queries
- Track referral traffic trends
- Evaluate business impact of AI-sourced traffic
- Adjust strategy based on observed patterns
What GEO Isn’t
GEO isn’t manipulating AI systems.
Keyword stuffing, fake authority signals, or content designed to trick rather than inform won’t work and may backfire. AI systems are designed to synthesize useful information—attempting to game them produces the same long-term risks as trying to game traditional search.
GEO isn’t abandoning traditional SEO.
Traditional search remains the majority of search traffic. AI-powered search is growing but hasn’t replaced traditional patterns. Maintain both.
GEO isn’t a known science.
We’re early in understanding how AI systems select sources. Current principles are derived from observation, not documented algorithms. What appears true now may change as systems evolve.
GEO isn’t guaranteed traffic.
Getting cited in AI responses doesn’t guarantee clicks. Users may get sufficient information from the AI response without visiting your site. Citation provides visibility and authority-building even without direct traffic, but don’t expect 1:1 traffic translation.
The Integration Perspective
GEO and traditional SEO share most fundamentals:
- Quality content that serves user needs
- Clear structure and organization
- Topical authority building
- Technical accessibility
The differences are emphasis:
- GEO rewards specific, citable statements
- GEO favors unique information that requires attribution
- GEO success is measured differently (citation presence, AI referral traffic)
For most organizations, this means applying a GEO lens to existing content strategy rather than creating a parallel strategy. Ask of each important piece of content: “Is this citable by AI systems? Does it contain unique, specific claims? Is it technically accessible to AI crawlers?”
If the answers are yes, you’re positioned for both traditional and AI search visibility. If not, improvements serve both channels.
The Nashville software company didn’t create a separate “GEO strategy.” They recognized their technical documentation was naturally citation-worthy and their marketing content wasn’t. They improved their marketing content to include more specific claims, real customer outcomes, and unique market data. The improvements helped both traditional conversion rates and AI citation frequency.
That’s the ideal GEO approach: better content that serves users, humans, and AI systems alike.
Resources
- OpenAI GPTBot documentation
https://platform.openai.com/docs/gptbot
- Perplexity: How Perplexity works
https://www.perplexity.ai/hub/faq
- Bing Webmaster Guidelines (covers Copilot)
https://www.bing.com/webmasters/help/webmaster-guidelines-30fba23a
GEO is an emerging practice without established best practices from AI platform providers. Recommendations reflect observed patterns and informed reasoning through early 2025. Approach with appropriate experimentation and measurement.