Getting cited by an LLM is not luck. ChatGPT, Gemini, and Perplexity each pull from different sources and reward different signals. I have spent the last few years learning exactly what each one wants.
I run platform-specific technical optimization to earn your brand citations inside AI-generated answers. Each LLM, ChatGPT, Gemini, Perplexity, Grok, and Claude, uses a distinct index, crawler, and ranking signal set, so I build you a separate implementation strategy for each.
Getting cited by an LLM is not luck. ChatGPT, Gemini, and Perplexity each pull from different sources and reward different signals. I have spent the last few years learning exactly what each one wants.
LLMs are no longer research tools: they are discovery and evaluation platforms. Users ask ChatGPT "who is the best SEO manager in the Philippines" and act on the answer without visiting a search results page.
Each LLM uses different data sources and citation logic. A single generic approach does not work across all five platforms. The LLM SEO service implements a distinct strategy per platform.
ChatGPT in Browse/Search mode pulls from Bing's index. In knowledge-answer mode, it draws from training data weighted toward authoritative, encyclopedic sources. Citation factors: Bing domain authority, neutral tone (no superlatives), FAQPage schema, presence on G2/Trustpilot/Wikipedia, and IndexNow for Bing rapid indexing.
Gemini is unique: it pulls exclusively from Google's own index. There is no shortcut: strong traditional SEO is the mandatory entry ticket. Because Google's AI features are rooted in its core ranking systems, the signals that matter are Google's own: featured snippet eligibility, E-E-A-T signals, page experience (LCP under 2.5s, INP under 200ms), section-level extractability, and Knowledge Graph entity presence. Gemini prioritizes pages that already rank in positions 1–5 on Google.
Perplexity actively crawls with PerplexityBot and weights freshness heavily: content updated within the last 30 days receives significantly higher citation probability. BLUF (Bottom Line Up Front) structure is critical: the direct answer must appear in the first 1–2 sentences of each section. Comparison and "best of" content achieves 32.5% higher citation rates on Perplexity.
Grok by xAI is unique in incorporating real-time X (Twitter) data alongside Bing's index. Social proof from X: brand mentions, engagement, and authority signals: influences citation. Grok weights recency more heavily than other LLMs. IndexNow protocol for Bing ensures rapid indexing of updated pages.
Claude uses the Brave Search index as its primary web source, plus curated training data from vetted publications. Publication authority and clear author attribution with Person schema are the key citation factors. llms.txt is implemented as a supplementary file for AI agents that read it.
I deliver technical implementation across all five platforms, content restructuring, schema deployment, and monthly Share of Model tracking in every engagement.
Efryll Carmelo is a Senior SEO Manager based in Iloilo, Philippines with 15+ years of experience serving US, Australian, and international clients. His LLM SEO practice is built on early adoption: implementing GEO and LLM optimization frameworks ahead of mainstream adoption, using the AI Search Ranking Strategy 2026 methodology as the technical foundation.
Implemented GEO and LLM-specific optimizations (robots.txt AI crawler allowlist, llms.txt, BLUF restructuring, Answer Capsules, FAQPage schema stacks) across client sites before these practices became mainstream. Listed GEO/AI Search as a core skill alongside traditional SEO disciplines.
Applied platform-specific strategies for all five LLMs: Bing Webmaster Tools for ChatGPT/Grok, traditional SEO foundations for Gemini, PerplexityBot access and freshness cadence for Perplexity, and ClaudeBot/llms.txt for Claude. Each platform receives a distinct implementation rather than a single generic approach.
Client case studies document +80% revenue growth attributable to SEO and a +400% increase in search impressions. AI citation appearances measured across ChatGPT and Perplexity for competitive service queries within 60–90 days of LLM SEO implementation. AI-referred visitors convert at 4.4x the organic baseline.
The deep dives above cover each platform; this is the summary worth saving.
| Platform | Primary Source | What Earns Citations |
|---|---|---|
| ChatGPT | Bing index | Bing indexing, listicle-format content, entity clarity |
| Google Gemini | Google index | Strong traditional SEO, featured-snippet eligibility, Knowledge Graph presence |
| Perplexity | Own crawler | PerplexityBot access, freshness, Reddit and community presence |
| Grok | X and web | X/Twitter activity and engagement signals |
| Claude | Brave Search index | Brave indexing, publication authority, author attribution |
LLM SEO (Large Language Model SEO) is the technical practice of optimizing a website's content, structure, and authority signals so that large language models: including ChatGPT, Google Gemini, Perplexity, Grok, and Claude: select your brand as a citation when generating answers to user queries. It goes deeper than general GEO by implementing platform-specific signals for each LLM's distinct crawling, indexing, and ranking behavior.
ChatGPT in Browse/Search mode pulls from Bing's index. Citation factors include: domain authority recognized by Bing, encyclopedic and neutral tone, presence on authoritative third-party platforms (G2, Trustpilot, Wikipedia), FAQPage schema, and IndexNow integration for Bing rapid indexing.
Google Gemini pulls exclusively from Google's own index: traditional SEO is the mandatory entry ticket. Google's guidance confirms AI features are rooted in its core ranking systems, so the signals that matter are Google's own: featured snippet eligibility, E-E-A-T signals, page experience, section-level extractability, and Knowledge Graph entity presence.
Perplexity uses its own PerplexityBot crawler plus Bing. Citation factors include: allowing PerplexityBot in robots.txt, content freshness (updates within 30 days), BLUF structure, and comparison-style content which generates 32.5% higher citation rates. Perplexity refreshes high-citation pages every 24–72 hours.
llms.txt is a root-level plain text file that lists your site's most authoritative pages in markdown format designed for AI agents to read and index. It is an emerging convention with uneven adoption: Google has publicly confirmed its systems do not use llms.txt, and no major AI platform has officially documented it as a ranking input. It is implemented as a low-cost hedge for AI agents that read it, never as a substitute for the signals that actually drive citations.
Grok uses Bing's index plus real-time X (Twitter) data. Citation factors include: allowing xAI-Grok crawler in robots.txt, IndexNow protocol for Bing rapid submission, active X/Twitter presence, and recency: Grok weights recent content more heavily than other LLMs.
Claude uses the Brave Search index as its primary web source, plus training data from vetted publications. Citation factors include: allowing ClaudeBot in robots.txt, Brave Search indexing, publication authority, and clear author attribution with Person schema markup. llms.txt is implemented as a low-cost extra, though no platform has confirmed using it as a ranking input.
LLM citation tracking uses Share of Model (SoM) measurement: running target queries across all five platforms monthly and recording citation frequency. Supporting data comes from GA4 AI referral traffic (chatgpt.com, ai.google.com, perplexity.ai, x.com) and Looker Studio dashboards for trend visualization.
LLM citation is now a direct business outcome. Book a free LLM SEO audit to see where your brand stands across all five AI platforms.
GEO Services
Full generative engine optimization strategy across all 5 AI platforms
AI SEO
AI-powered execution tools + AI search visibility strategy
Content Optimization
BLUF restructuring and Answer Capsule implementation for existing content
SEO Audit
Technical GEO checklist including robots.txt AI bot audit and schema validation