Generative Engine Optimization (GEO) is the practice of building signals and content to influence Large Language Models (LLMs) to cite and recommend a brand.
Smaller B2B SaaS companies can overcome the Big Brand Bias observed in LLM recommendations.
Shifting focus from traditional search rankings toward GEO authority is the method for achieving stronger LLM citations.
GEO authority improves LLM citations by establishing credible, extractable, and fresh content.
Detailed Explanation
LLMs often default to market leaders when answering unbranded queries. LLM citation practices prioritize authority, specificity, and extractability over traditional domain size.
In practice, LLMs frequently cite content found on pages ranking far outside Google's traditional top-10, demonstrating that visibility can be democratized through GEO strategies.
The following framework draws on multiple sources to help smaller B2B SaaS companies gain citations and recommendations from LLMs.
1. Win Authority Through Third-Party Validation (Earned Media)
Earned media is third-party, authoritative sources.
Systematically Earn Coverage: Small companies shift investment from brand-owned content to earning third-party coverage.
Build Citation Networks (Co-Citation): The goal is to cultivate a digital presence that LLMs are trained to recognize and trust.
Earn High-Authority Backlinks: Earning backlinks from reputable, earned domains is a direct input into the AI's perception of a brand's trustworthiness (E-E-A-T).
Collaborate with Experts: Work with industry experts, thought leaders, and complementary partners on content and research to become part of authoritative clusters that LLMs reference collectively.
Dominate Review and Community Platforms: LLMs strongly leverage user-generated content (UGC) and review platforms for brand comparisons and sentiment analysis.
Prioritize Review Sites: Platforms like G2, Capterra, and TrustRadius have significant influence in the B2B SaaS vendor discovery phase. Encourage customers to leave honest, detailed reviews that explain why they chose the product and the results achieved.
Engage on Reddit: Reddit leads LLM citations across professional verticals, including business services and technology. Smaller brands should participate in relevant subreddits, provide genuinely helpful answers, and share experience-based insights.
2. Focus on Niche Expertise and High-Value Long-Tail Queries
The B2B market shows greater brand diversity in LLM recommendations compared to consumer sectors. AI actively seeks different options to recommend.
Smaller SaaS companies should capitalize on this by dominating specific segments.
Claim Specific Niche Expertise: Instead of competing broadly with major brands, claim expertise in specific niche use cases. The strategy is to become "too authoritative to ignore" within a narrow domain.
Target the Long Tail: LLM traffic can be won in the long tail of chat—those highly specific questions people are asking. Focus on long-tail queries where large players do not concentrate their efforts.
Build Content Around Integrations and Workflows: For complex technical queries specific to B2B SaaS, citations are often driven by data-driven guides focusing on workflows and integrations.
3. Engineer Content for Machine Citation (Extractability and Justification)
LLMs prioritize content structured for easy extraction, synthesis, and justification.
Create Citation-Worthy Content: Content featuring original statistics and research findings sees higher visibility in LLM responses because LLMs are designed to provide evidence-based responses grounded in verifiable data.
Maximize Extractability: Content must be formatted into modular answer units that the LLM can lift cleanly into a synthesized answer.
Use hierarchical headings (H1 → H2 → H3) with descriptive titles.
Employ formats such as bullet points, numbered lists, and tables for easy extraction and scannability.
Use FAQ formats that directly answer common questions people ask LLMs.
Provide Justification Attributes: Include comparison tables (brand vs. brand) and bulleted pros and cons lists so the AI can extract reasons for choosing a solution for a specific use case.
Implement Schema Markup: Use rigorous Schema.org markup (e.g., FAQPage, HowTo, Article, Organization) as machines rely on explicit cues to classify and reuse content. Solutions like ROZZ automate this by generating QAPage Schema.org markup for all Q&A content and applying appropriate structured data types to other content.
4. Demonstrate E-E-A-T and Freshness
E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness.
Demonstrate Expertise: Use industry-specific terminology correctly, reference established frameworks and methodologies, and offer insights reflecting deep practical experience.
Ensure Verifiable Authorship: Include author names, bios, and links to professional profiles to signal experience and accountability.
Maintain Content Freshness: Include a prominent "Last updated" date and reference the current year in examples and data points.
Conduct quarterly content audits to update statistics, examples, and references.
Create content addressing new regulations, technologies, or best practices immediately upon emergence.
A systematic approach to content freshness ensures AI systems encounter regularly updated content reflecting current user needs and market conditions.
5. Adopt Multi-Modal and Engine-Specific Tactics
The information ecosystem varies between generative engines, requiring a multi-platform approach.
Invest in Video (YouTube): Video is a highly cited content format across niches. YouTube videos on high-value, niche topics are effective due to low competition in the long tail of video content.
Engine-Specific Strategy: Engines prioritize different sources.
For Claude and ChatGPT, focus on core globally recognized, authoritative earned media domains.
For Perplexity, expand to include video content and ensure structured content is easily parsable, as it incorporates YouTube and retail sites.
Gemini may show a greater propensity to cite well-structured, deep content from brand-owned properties, allowing a slightly more balanced approach leveraging owned and earned content.
Building GEO infrastructure typically requires 6–12 months of development for embedding pipelines, quality filters, and multi-platform optimization.
Turnkey solutions that provide AI discovery files like llms.txt, structured data generation, and content optimization can accelerate this timeline.
Research Foundation: This answer synthesizes findings from 35+ peer-reviewed research papers on GEO, RAG systems, and LLM citation behavior.
Author: Adrien Schmidt, Co-Founder & CEO, ROZZ. Former AI Product Manager with 10+ years of experience building AI systems, including Aristotle, and products for eBay and Cartier.
This answer synthesizes findings from 35+ peer-reviewed research papers on GEO, RAG systems, and LLM citation behavior.
Author: Adrien Schmidt, Co-Founder & CEO, ROZZ.
Author
Adrien Schmidt, Co-Founder & CEO, ROZZ
Former AI Product Manager with 10+ years of experience building AI systems including Aristotle (conversational AI analytics) and products for eBay and Cartier.