How a B2B SaaS Company Increased AI Visibility by 40%

The Challenge

Our client had invested heavily in traditional SEO and content marketing. Their organic search rankings were strong, but they noticed something concerning: when prospects asked AI assistants for recommendations in their category, they were rarely mentioned.

Their AI Selection Rate - how often AI systems recommended them when relevant - was just 12%, compared to 45% for their main competitor. This gap was becoming increasingly significant as more B2B buyers began their research journey through AI-assisted search.

Our Approach

We started with a GEO Readiness Audit to map exactly where the gaps were:

1. Latent Space Analysis

We analyzed how AI models perceived our client versus competitors. The findings were illuminating: while traditional search engines understood the company well, AI systems had fragmented and sometimes contradictory information about their offerings.

2. Entity Relationship Mapping

We identified weak or missing connections in knowledge graphs. The company’s product categories weren’t properly linked to their brand entity, and several key differentiators were absent from AI training data entirely.

3. Technical Infrastructure Review

We assessed Schema.org implementation and machine-readability. While basic markup existed, it was incomplete and inconsistent across the site, leading to poor AI comprehension of their value proposition.

The Implementation

Based on the audit findings, we implemented a comprehensive Relevance Engineering program:

• Schema.org Enhancement: Comprehensive markup across all key pages, properly connecting products, services, and company information
• Entity Optimization: Strengthened knowledge graph presence through strategic content and citation building
• Content Authority Signals: Aligned content structure with AI understanding patterns
• llms.txt Implementation: Direct AI discoverability protocol for explicit machine communication

Results

Within three months of implementation:

• AI Selection Rate increased from 12% to 52% - a transformation that put them ahead of their main competitor
• Featured in ChatGPT responses for 12 of 15 target queries - up from just 2 previously
• Knowledge panel accuracy improved from 60% to 95% - ensuring AI systems now had correct, comprehensive information
• Organic traffic from AI-referred sources increased 3x - as more users discovered them through AI recommendations

Key Takeaway

Traditional SEO and GEO are complementary, not competing. Our client’s strong content foundation meant the Relevance Engineering work could build on existing assets rather than starting from scratch. The investment in traditional SEO created the substrate; Relevance Engineering made it readable to the new generation of AI systems that are increasingly mediating buyer decisions.