AI Search Optimization

AI search tools (Perplexity, ChatGPT Browse, Bing Copilot, Google AI Overviews) index and surface content differently than traditional search engines. We optimize the technical and structural layer that determines whether your platform gets cited in AI-generated answers. This is engineering work: entity relationships, semantic architecture, structured data, and crawl accessibility for AI agents. Most platforms aren't optimized for this yet. That's the window.

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Is this you?

You're building a SaaS platform, a marketplace, or an information-rich product and want to surface in AI-generated search results, not just blue links. Your content exists. The technical layer that makes it readable and quotable by AI tools doesn't. You want to move while competitors haven't caught up.

How we work

Our process

AI crawl audit

We assess how AI crawlers currently access your platform: what they can read, what's blocked, what's structurally unclear, and where entity relationships are missing.

Architecture

We design the semantic structure: entity relationships, content hierarchy, structured data types, and the llms.txt specification for your platform.

Implement

We build it. Schema markup, llms.txt, semantic content architecture adjustments. Changes are tested against AI crawler behavior, not just validation tools.

Monitor

We track AI-driven referral traffic (Perplexity, ChatGPT, Bing) and citation frequency. Ongoing refinement as AI search behavior evolves.

What you get

What you walk away with

Platform accessible and interpretable by all major AI crawlers
Structured data enabling entity extraction and citation by AI tools
llms.txt implemented and verified for AI agent accessibility
Monitoring in place to track AI-driven traffic and citation growth

Full deliverables

AI crawl accessibility audit (GPTBot, PerplexityBot, Bingbot AI, Googlebot)
llms.txt specification and implementation
Schema and entity relationship optimization (Organization, Product, FAQ, HowTo)
Semantic content architecture recommendations
Structured data for AI extraction across applicable page types
Referral traffic monitoring setup (Perplexity, ChatGPT, Bing AI)

Technologies

Schema.orgllms.txtJSON-LDGoogle AI OverviewsPerplexityBing Copilot

Typical timeline: 2–6 weeks depending on platform complexity
Engagement: Project-based, often paired with Technical SEO

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Get cited by Perplexity, ChatGPT, and Google AI Overviews.

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AI Search Optimization

Start a project

Our process

AI crawl audit

We assess how AI crawlers currently access your platform: what they can read, what's blocked, what's structurally unclear, and where entity relationships are missing.

Architecture

We design the semantic structure: entity relationships, content hierarchy, structured data types, and the llms.txt specification for your platform.

Implement

We build it. Schema markup, llms.txt, semantic content architecture adjustments. Changes are tested against AI crawler behavior, not just validation tools.

Monitor

We track AI-driven referral traffic (Perplexity, ChatGPT, Bing) and citation frequency. Ongoing refinement as AI search behavior evolves.

Full deliverables

AI crawl accessibility audit (GPTBot, PerplexityBot, Bingbot AI, Googlebot)

llms.txt specification and implementation

Schema and entity relationship optimization (Organization, Product, FAQ, HowTo)

Semantic content architecture recommendations

Structured data for AI extraction across applicable page types

Referral traffic monitoring setup (Perplexity, ChatGPT, Bing AI)