Trust Stack™ > AI Discoverability

Trust signal alignment for AI discoverability

Optimize your entire credibility footprint specifically for AI-driven discovery. Ensure your content gets consistently retrieved, surfaced, and cited by LLMs like ChatGPT, Gemini, and Bing Chat.

Reinforce Your Authority

“Growth Marshal turned SEO into a core component of our user acquisition strategy.”

Alex Melnyk
CEO, Modality

“Growth Marshal is the right partner for any startup looking to grow organically.”

Kevin Harrington
Co-founder, ChatVault

“They wasted no time digging into our KPIs, go-to-market, and competition.”

Jenn Stroupe
Co-founder, Pinnacle

If AI can't retrieve you, how will your customers know that you exist?

"In an AI-first world, visibility isn't just about rankings—it's about retrieval. Large Language Models (LLMs) like ChatGPT, Gemini, and Bing Chat are reshaping how information is surfaced, cited, and trusted. If your trust signals aren’t aligned for AI systems, your brand becomes invisible at the moment of search.

AI Retrieval Relies on Semantic Matching, Not Just Keywords

Traditional SEO chases keywords; AI retrieval prioritizes semantic context. If your content isn't structured to align with AI prompt embeddings, it won't surface—no matter how good your rankings are.

Prompt Surfaces Control AI Discoverability

LLMs extract content from titles, headers, FAQs, and intros. Without optimizing these surfaces, you miss critical opportunities to match user prompts and trigger citations.

Embeddings Define Visibility Across RAG Pipelines

Retrieval-Augmented Generation (RAG) systems convert content into vectors. Your site needs to generate embeddings that match the informational needs of AI retrievers—or risk being ignored.

Trust Signals Influence Which Brands AI Cites

Schema markup, entity authority, and third-party citations shape how LLMs evaluate your credibility. But it’s not just about having trust signals—it’s about aligning them for AI systems that think differently from human users.

AI Discoverability isn’t Static. It’s a Moving Target

LLMs evolve rapidly. Ongoing simulation testing across AI platforms is critical to maintaining—and expanding—your brand's surfacing and citation footprint.

How we engineer your brand for AI retrieval

To secure your place in the AI-driven web, we go beyond traditional optimization—strategically aligning your trust signals, semantic surfaces, and authority footprint to maximize retrieval, surfacing, and citation across leading AI-native platforms.

☑️ Prompt Surface Optimization
We restructure your critical content surfaces (titles, headers, FAQs, intros) to align semantically with AI prompt retrieval patterns—maximizing the odds that you become the first answer LLMs reach for.

☑️ RAG Retrieval Simulation Reports
We run retrieval tests across ChatGPT with browsing, Perplexity, Bing Copilot, and You.com to measure your brand’s actual surfacing rates—identifying retrieval gaps and optimizing for real-world AI behavior.

☑️ Trust Signal to LLM Correlation Analysis
We map your current trust signals (schema, citations, Wikidata links) to their impact on AI retrieval behavior—prioritizing the signals that LLMs actually reward in surfacing decisions.

☑️ Authority Context Packaging
We create structured, high-trust mini-sections inside your website—embedding-rich, entity-dense content specifically designed to be snapshotted, cited, and favored by AI retrievers.

☑️ Embedding Footprint Audit
We analyze how your brand’s key pages and content convert into embeddings in LLMs—ensuring your site semantically matches the vectors LLMs rely on when answering prompts and generating citations.