The five engines we track
  • Google AI Overviews13%+ of Google queries
  • ChatGPT300M weekly users
  • Perplexity5× YoY query volume
  • GeminiWorkspace default
  • ClaudeTechnical / B2B
Mention Layer monitors all five weekly across 30+ priority keywords per client. The dashboard you actually run from.
AI Search Optimization

Search just turned into a synthesis problem.
Optimize for the new mechanics.

Five major AI engines retrieve, synthesize, and cite sources differently. The optimization framework that works on all five is structural — schema graphs, citation- ready content, entity authority — paired with engine- specific tuning. Here's the playbook.

What changed in search since 2024
5
AI engines now intercept buyer-research queries before Google ever loads
32%
of B2B buyer journeys now begin in an AI engine, not a search box
1.4×
more likely a buyer is to mention a vendor by name when AI surfaced it first
0
of those interactions appear in your traditional analytics stack
Definition

What is AI search optimization?

AI search optimization is the practice of structuring your website, content, and external citations so that AI-powered search engines retrieve, synthesize, and cite your business when users ask category-relevant questions.

Where traditional search returns ten ranked links and lets the user choose, AI search returns one synthesized answer with a citation footnote. The optimization implications cascade from there. Schema becomes more important than meta descriptions. Citation density beats keyword density. Entity disambiguation beats page-level relevance. The buyer experience compresses from "research and click" to "ask and decide."

The discipline overlaps heavily with traditional SEO — strong content and clean technical foundations underpin both — but adds an additional structural layer specifically engineered for AI extraction and citation. We treat them as one integrated workstream because the same operator who ranks your blue links is the operator who engineers your AI citations.

The five-engine matrix

Each engine retrieves and cites differently.
Optimize accordingly.

Engine 01

Google AI Overviews

Above the organic results
13%+
of Google queries now show one
Retrieval

Google's full search index, weighted toward strong E-E-A-T signals and pages with structured FAQ + HowTo schema.

Citation behavior

Cited URLs appear as a small footer panel below the synthesized answer. Click-through is meaningful but lower than position #1 organic.

Tactical lever

Google AI Overviews citations are won the same way featured snippets are won — clear question-formatted H2s, definition-style answer paragraphs, FAQ schema. The fastest of all five engines to optimize for.

Engine 02

ChatGPT

Largest generative engine
300M+
weekly active users
Retrieval

Browsing mode queries Bing's index plus OpenAI's curated source list. Standard mode answers from training-corpus alone.

Citation behavior

Mixed. Browsing mode cites with linked footnotes. Training-corpus answers may name brands without citation links.

Tactical lever

Bing SEO is the most-overlooked tactical lever. Plus tier-1 publication mentions for training-corpus authority. We cover both in /chatgpt-seo.

Engine 03

Perplexity

Citation-by-default engine
query volume vs same period 2025
Retrieval

Internal index that draws from the broader web with a strong recency bias. Refreshes aggressively.

Citation behavior

Every answer cites sources by default with linked footnotes. Highest click-through rate of any AI engine.

Tactical lever

Recency-weighted, so freshness matters more here. Update dateModified, ship cadence content, and earn fresh third-party citations.

Engine 04

Gemini

Google's native AI assistant
Workspace
default for enterprise users
Retrieval

Google's index directly — same retrieval surface as traditional Google search and AI Overviews.

Citation behavior

Inline citations linking to source URLs in most response modes.

Tactical lever

Strong baseline Google SEO is the entire ballgame here. If you rank in Google for a query, you're in the running for Gemini citation. Schema graph and entity disambiguation widen the lead.

Engine 05

Claude

Technical / B2B audience
Anthropic
the engine we use ourselves
Retrieval

Web tool uses Brave Search and similar privacy-respecting infrastructure. Smaller index than Bing or Google.

Citation behavior

Linked citations when web tool is invoked. Strong preference for authoritative, verifiable sources.

Tactical lever

Schema graph + author entity signals matter most. Claude weighs verifiable expertise heavily — Person schema with sameAs links to Forbes / books / podcasts is high-leverage here.

The XD framework

Six steps that work on all five engines.
Engine-specific tuning layers on top.

Step 01

Baseline measurement

Mention Layer audit across all five engines for 30+ priority keywords. Citation count, citation context, sentiment, source mapping, competitor benchmarks. The 'before' snapshot every engagement is measured against.

Step 02

Schema graph upgrade

Site-wide @graph JSON-LD chaining Organization → Person → Service/Article. The single highest-leverage on-site change for entity disambiguation across all five engines.

Step 03

FAQ + AEO content layer

FAQ schema with 4-8 Q&A pairs per page. Direct-answer paragraphs under every H1. Question-formatted H2s. The patterns engines extract verbatim into responses.

Step 04

llms.txt + ai.txt

Markdown manifests at site root declaring authoritative URLs, author identity, content licensing. Most sites still don't ship these. AI crawlers reward sites that do.

Step 05

Authority-citation campaigns

PressForge-driven outreach for tier-1 publication mentions, expert-comment placements, podcast appearances, original-data research. The third-party signals that train AI models.

Step 06

Engine-specific tuning

Bing SEO for ChatGPT browsing. Recency cadence for Perplexity. Author-entity sameAs for Claude. Featured-snippet patterns for Google AI Overviews. The deltas after the framework.

What doesn't work

The four patterns we see most agencies get wrong.

Audit signals from 50+ engagements. Each is fixable in the first 60 days, but each tanks results when missed.

Treating all engines as one

ChatGPT browsing leans on Bing. Gemini leans on Google. Perplexity leans on recency. Generic 'GEO content' that ignores those differences captures none of the engine-specific upside.

Skipping baseline measurement

If you don't know your starting citation share, you can't know if any tactic is working. We've audited engagements that ran for 6 months with no measurement — couldn't tell what moved.

On-site only, no authority work

Schema and FAQs alone produce a ceiling. Without third-party citations from authoritative sources, training-corpus AI engines (ChatGPT, Claude) won't learn to associate your brand with the topic. Two workstreams, run together.

Producing AI-generated content to optimize for AI search

Google's helpful-content systems catch unedited LLM output and penalise the entire site. Our editorial pass strips every AI tell. AI as leverage; humans as final voice.

Common questions

What teams ask before scoping an AI search engagement.

AI search optimization is the practice of structuring your website, content, and external citations so that AI-powered search engines — ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews — retrieve, synthesize, and cite your business when users ask category-relevant questions. It's the operational discipline beneath the marketing term 'GEO'. Where traditional search returns ten ranked links, AI search returns one synthesized answer with a footnote of cited sources. AI search optimization is how you become one of those cited sources.

Three architectural differences. First, retrieval: traditional search retrieves a ranked list of documents; AI search retrieves a smaller set of documents and synthesizes them into a single response. Second, presentation: traditional search shows you the documents and lets you choose; AI search shows you an answer and a citation footer. Third, intent: traditional search assumes the user will click through and read; AI search assumes the user has already gotten what they need by the time they finish reading. The optimization implications cascade from there — schema becomes more important than meta descriptions, citation density beats keyword density, and entity disambiguation beats page-level relevance.

All five major engines, with priority based on your audience. Google AI Overviews appears above the organic results on roughly 13% of queries (and growing) — biggest near-term opportunity. ChatGPT has 300M+ weekly active users and is now the dominant top-of-funnel research engine for B2B buyers. Perplexity sends meaningful click-throughs because every answer cites sources by default. Gemini integrates across Google Workspace and is the default for many enterprise users. Claude (which we use ourselves) drives a smaller but extremely high-intent technical audience. Optimize for all five — the underlying patterns largely overlap — but tune emphasis to where your buyers actually live.

Yes, with significant overlap. ChatGPT browses Bing's index plus a curated source list. Perplexity browses an internal index that draws from the broader web with a strong recency bias. Gemini draws from Google's index — so traditional Google SEO directly influences Gemini visibility. Claude's web tool uses Brave and similar privacy-respecting search infrastructure. Google AI Overviews pulls from Google's indexed pages, weighted toward sites with strong E-E-A-T signals. Strong baseline SEO (Google + Bing) underpins all five. The differences are most pronounced in citation behavior, not retrieval source.

Schema graph + FAQ markup, in that order. A site-wide @graph JSON-LD that chains Organization → Person → Article/Service tells AI engines exactly what entity your site represents and who's responsible for the content. FAQ markup with FAQPage schema gets extracted near-verbatim into AI answers across all five engines. After those, the 50-word direct-answer paragraph under every H1, citation-friendly bullet blocks, and question-formatted H2s. These five changes ship in the first 30 days of every engagement and produce measurable lift on Perplexity and Google AI Overviews fastest.

We use Mention Layer (a SaaS Joel built for exactly this) to monitor 30-50 priority keywords across all five engines weekly. For each keyword, the dashboard tracks: was your brand mentioned, was it cited with a link, what was the citation context, what was the sentiment, which page was the source, and how does your citation share compare to the top three competitors. The composite output is a citation share percentage — your share of voice across AI answers in your category, tracked over time. We report this monthly alongside traditional GSC and GA4 metrics.

You can do the on-site portion yourself if you have a senior in-house operator: schema graph, FAQ markup, llms.txt, direct-answer paragraphs, question-formatted H2s. The harder workstream is external — earning third-party citations from tier-1 publications, expert-comment placements, original-data research that journalists quote. That's the work that compounds long-term, and it's the work most in-house teams don't have the relationships or production cadence to run consistently. Most agencies (us included) earn fees on that workstream, not the on-site retrofit.

Faster than traditional SEO for direct citation lift. Perplexity citations can shift in 30 days because its index updates aggressively. Google AI Overviews citations move on similar timelines because they pull from already-indexed Google content. ChatGPT and Claude move slower — 90-180 days — because their citation behavior is heavily influenced by training-corpus authority that takes longer to accrue. Realistic milestones: 30-60 days for first measurable Perplexity and Google AI Overview lift, 90-180 days for ChatGPT visibility, 12+ months for category-level brand association across all engines.

Get cited across all five engines

Five engines. One framework.
Audit the gap. Close it.

Mention Layer baseline across ChatGPT, Perplexity, Gemini, Claude, and Google AI Overview. We benchmark you against the top three competitors per keyword and ship a 90-day plan to close the citation gap. Joel reviews every audit.