Real Results in AI Search
See how we help brands become the default answer in AI search engines.
SaaS Platform: From Zero to Primary Citation in ChatGPT
Client:
B2B SaaS platform (confidential)
Industry:
Developer Tools
Timeline:
3 months
Service:
Conversational SEO Optimization
The Challenge
Despite ranking well for target keywords on Google, the client was completely invisible in AI search engines. When potential customers asked ChatGPT or Perplexity about their category, competitors were mentioned—not them.
What We Found
- Zero citations in ChatGPT, Perplexity, or Claude for category-related queries
- Content was keyword-focused, not entity-focused
- Frontend architecture used heavy client-side rendering, making content hard for AI crawlers to access
- No structured data beyond basic schema.org markup
- Content wasn't chunked for optimal LLM retrieval
What We Did
- Re-architected content around entities and concepts, not keywords
- Implemented edge rendering for critical content pages
- Added comprehensive JSON-LD structured data
- Restructured content into semantic clusters with clear entity relationships
- Created source-worthy content that answered common questions in the category
- Optimized internal linking for AI context understanding
Results
- ✓ChatGPT Citations: From 0% to 60% of category queries now mention the client
- ✓Perplexity Citations: Client now appears in 45% of relevant queries with source links
- ✓Lead Quality: 3x increase in qualified leads mentioning "AI search" or "ChatGPT" as discovery channel
- ✓Authority Signals: Improved entity graph representation across knowledge bases
"We didn't realize how much we were missing by not being visible in AI search. Now when our target customers ask AI tools about our category, we're the answer. The quality of leads has improved significantly." — Client CMO
B2B Dev Tools: Winning in Perplexity Search
Client:
Developer tools company (confidential)
Industry:
Software Development
Timeline:
4 months
Service:
AI Search Audit + Optimization
The Challenge
The client had extensive technical documentation but wasn't being cited by AI tools when developers asked technical questions. Their content was comprehensive but not structured for AI retrieval.
What We Found
- Technical documentation existed but wasn't optimized for AI consumption
- Content was in long-form articles, not chunked for retrieval
- Missing structured data for code examples and technical concepts
- No clear entity relationships in content structure
What We Did
- Restructured documentation into semantic chunks optimized for RAG
- Added technical schema markup for code examples and API documentation
- Created Q&A format content answering common developer questions
- Implemented custom AI chatbot that reinforced authority
- Optimized for Perplexity's citation format (source links)
Results
- ✓Perplexity Citations: 5x increase in source citations for technical queries
- ✓Documentation Traffic: 40% increase in traffic from AI search referrals
- ✓Developer Adoption: Measurable increase in sign-ups mentioning AI tools as discovery method
Early Experiments & Learnings
AI search optimization is still emerging. We're continuously experimenting and learning what works. Here's what we've discovered:
- •Entity-first content consistently outperforms keyword-first content in AI citations
- •Source-worthy content (content that answers questions directly) gets cited more than promotional content
- •Technical architecture matters: Server-rendered or edge-rendered content performs better than client-rendered content
- •Structured data at scale helps AI tools understand context and relationships
- •Content chunking optimized for RAG significantly improves citation rates
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