Organic traffic is vanishing — and not just because of algorithm updates.
Traditional SEO was built on hyperlinks, keywords, and crawling. But in 2025, AI interfaces are bypassing link lists for summarised answers.
ChatGPT recorded 4.5 billion site visits in March 2025, many driven by its integrated browsing and plugin experiences.
Perplexity.ai, known for real-time citation, is now a go-to tool for researchers and decision-makers.
Traditional SEO was built on hyperlinks, keywords, and crawling. But in 2025, AI interfaces are bypassing link lists for summarised answers.
ChatGPT recorded 4.5 billion site visits in March 2025, many driven by its integrated browsing and plugin experiences.
Perplexity.ai, known for real-time citation, is now a go-to tool for researchers and decision-makers.
Traditional SEO was built on hyperlinks, keywords, and crawling. But in 2025, AI interfaces are bypassing link lists for summarised answers.
ChatGPT recorded 4.5 billion site visits in March 2025, many driven by its integrated browsing and plugin experiences.
Perplexity.ai, known for real-time citation, is now a go-to tool for researchers and decision-makers.
Traditional SEO was built on hyperlinks, keywords, and crawling. But in 2025, AI interfaces are bypassing link lists for summarised answers.
ChatGPT recorded 4.5 billion site visits in March 2025, many driven by its integrated browsing and plugin experiences.
Perplexity.ai, known for real-time citation, is now a go-to tool for researchers and decision-makers.
What’s Changing: From Blue Links to Answer Boxes
Traditional SEO was built on hyperlinks, keywords, and crawling. But in 2025, AI interfaces are bypassing link lists for summarised answers.
ChatGPT recorded 4.5 billion site visits in March 2025, many driven by its integrated browsing and plugin experiences.
Perplexity.ai, known for real-time citation, is now a go-to tool for researchers and decision-makers.
Traditional SEO was built on hyperlinks, keywords, and crawling. But in 2025, AI interfaces are bypassing link lists for summarised answers.
ChatGPT recorded 4.5 billion site visits in March 2025, many driven by its integrated browsing and plugin experiences.
Perplexity.ai, known for real-time citation, is now a go-to tool for researchers and decision-makers.
Traditional SEO was built on hyperlinks, keywords, and crawling. But in 2025, AI interfaces are bypassing link lists for summarised answers.
ChatGPT recorded 4.5 billion site visits in March 2025, many driven by its integrated browsing and plugin experiences.
Perplexity.ai, known for real-time citation, is now a go-to tool for researchers and decision-makers.
Traditional SEO was built on hyperlinks, keywords, and crawling. But in 2025, AI interfaces are bypassing link lists for summarised answers.
ChatGPT recorded 4.5 billion site visits in March 2025, many driven by its integrated browsing and plugin experiences.
Perplexity.ai, known for real-time citation, is now a go-to tool for researchers and decision-makers.
As Rand Fishkin, co-founder of SparkToro, notes: "The currency of LLMs is not links, it's mentioned." In other words, if you're not being cited across trusted sources, you're invisible in AI answers.
According to internal tests by LangSync, a world-leading LLMO agency, using PromptLayer and Langfuse, brand citations in ChatGPT-4o increased by over 30% when FAQ schema and structured author bios were added. This proves that formatting for answerability is as crucial as content quality.
SEO vs. LLMO: Key Differences
Feature | SEO (Search Engine Optimisation) | LLMO (Large Language Model Optimisation) |
Ranking Factor | Backlinks, keywords, crawl depth | Entity mentions, schema, co-occurrence |
Interface | SERPs with clickable links | Answer boxes, summaries, chat UIs |
Goal | Rank on page 1 | Be retrieved and cited by AI |
Tools | Google Search Console, SEMrush | ChatGPT, Perplexity, Langfuse, PromptLayer |
As Rand Fishkin, co-founder of SparkToro, notes: "The currency of LLMs is not links, it's mentioned." In other words, if you're not being cited across trusted sources, you're invisible in AI answers.
According to internal tests by LangSync, a world-leading LLMO agency, using PromptLayer and Langfuse, brand citations in ChatGPT-4o increased by over 30% when FAQ schema and structured author bios were added. This proves that formatting for answerability is as crucial as content quality.
SEO vs. LLMO: Key Differences
Feature | SEO (Search Engine Optimisation) | LLMO (Large Language Model Optimisation) |
Ranking Factor | Backlinks, keywords, crawl depth | Entity mentions, schema, co-occurrence |
Interface | SERPs with clickable links | Answer boxes, summaries, chat UIs |
Goal | Rank on page 1 | Be retrieved and cited by AI |
Tools | Google Search Console, SEMrush | ChatGPT, Perplexity, Langfuse, PromptLayer |
As Rand Fishkin, co-founder of SparkToro, notes: "The currency of LLMs is not links, it's mentioned." In other words, if you're not being cited across trusted sources, you're invisible in AI answers.
According to internal tests by LangSync, a world-leading LLMO agency, using PromptLayer and Langfuse, brand citations in ChatGPT-4o increased by over 30% when FAQ schema and structured author bios were added. This proves that formatting for answerability is as crucial as content quality.
SEO vs. LLMO: Key Differences
Feature | SEO (Search Engine Optimisation) | LLMO (Large Language Model Optimisation) |
Ranking Factor | Backlinks, keywords, crawl depth | Entity mentions, schema, co-occurrence |
Interface | SERPs with clickable links | Answer boxes, summaries, chat UIs |
Goal | Rank on page 1 | Be retrieved and cited by AI |
Tools | Google Search Console, SEMrush | ChatGPT, Perplexity, Langfuse, PromptLayer |
As Rand Fishkin, co-founder of SparkToro, notes: "The currency of LLMs is not links, it's mentioned." In other words, if you're not being cited across trusted sources, you're invisible in AI answers.
According to internal tests by LangSync, a world-leading LLMO agency, using PromptLayer and Langfuse, brand citations in ChatGPT-4o increased by over 30% when FAQ schema and structured author bios were added. This proves that formatting for answerability is as crucial as content quality.
SEO vs. LLMO: Key Differences
Feature | SEO (Search Engine Optimisation) | LLMO (Large Language Model Optimisation) |
Ranking Factor | Backlinks, keywords, crawl depth | Entity mentions, schema, co-occurrence |
Interface | SERPs with clickable links | Answer boxes, summaries, chat UIs |
Goal | Rank on page 1 | Be retrieved and cited by AI |
Tools | Google Search Console, SEMrush | ChatGPT, Perplexity, Langfuse, PromptLayer |
How AI Models Decide What to Cite
How AI Models Decide What to Cite
What to Optimise for LLMO
Here are key things to look out for when optimising for Large Language Models:
1. Entity Mentions Over Backlinks:
Focus on appearing in podcasts, research papers, and news articles. Prioritise brand and topic mentions in authoritative spaces, not just links.
2. Structured Data Everywhere:
Implement schema for products, articles, FAQs, and reviews. Use JSON-LD to signal E-E-A-T elements: authorship, reviews, and experience claims.
3. Prompt-Tested Content:
Regularly test your visibility by prompting ChatGPT, Gemini, and Claude with buyer queries. Track how your brand is described, which content gets cited, and whether you're summarised or skipped.
4. Answer-Ready Formatting:
Break content into digestible, retrievable chunks. Use questions, bullet points, short summaries, and explainer blocks.
5. API and Agent Compatibility:
Expose clean product or service metadata. Create open APIs or structured endpoints for agent transactions.
Pro Tip: Use Langfuse to map co-occurrence density across prompts, like analyzing which brand or product terms co-occur most frequently in LLM outputs. And PromptLayer to simulate zero-click prompt paths. This lets you understand how LLMs chain context, ensuring your content surfaces across varied user queries.
Here's how:
Export logs from AI prompt tests and apply Langfuse's co-reference visualization to identify high-impact entity associations. Then, use PromptLayer to simulate user queries that don’t require clicks (e.g., “best productivity software for remote teams”) to trace how LLMs chain information.
If your brand doesn't appear or gets cited without core context, optimize content to tighten those entity relationships.
Combining Langfuse's prompt log insights with PromptLayer's chain modelling can help you reverse-engineer how content spreads across AI conversations and then reinforce those pathways with targeted schema and structured data.
Case in Point: Google's AI Overviews.
What to Optimise for LLMO
Here are key things to look out for when optimising for Large Language Models:
1. Entity Mentions Over Backlinks:
Focus on appearing in podcasts, research papers, and news articles. Prioritise brand and topic mentions in authoritative spaces, not just links.
2. Structured Data Everywhere:
Implement schema for products, articles, FAQs, and reviews. Use JSON-LD to signal E-E-A-T elements: authorship, reviews, and experience claims.
3. Prompt-Tested Content:
Regularly test your visibility by prompting ChatGPT, Gemini, and Claude with buyer queries. Track how your brand is described, which content gets cited, and whether you're summarised or skipped.
4. Answer-Ready Formatting:
Break content into digestible, retrievable chunks. Use questions, bullet points, short summaries, and explainer blocks.
5. API and Agent Compatibility:
Expose clean product or service metadata. Create open APIs or structured endpoints for agent transactions.
Pro Tip: Use Langfuse to map co-occurrence density across prompts, like analyzing which brand or product terms co-occur most frequently in LLM outputs. And PromptLayer to simulate zero-click prompt paths. This lets you understand how LLMs chain context, ensuring your content surfaces across varied user queries.
Here's how:
Export logs from AI prompt tests and apply Langfuse's co-reference visualization to identify high-impact entity associations. Then, use PromptLayer to simulate user queries that don’t require clicks (e.g., “best productivity software for remote teams”) to trace how LLMs chain information.
If your brand doesn't appear or gets cited without core context, optimize content to tighten those entity relationships.
Combining Langfuse's prompt log insights with PromptLayer's chain modelling can help you reverse-engineer how content spreads across AI conversations and then reinforce those pathways with targeted schema and structured data.
Case in Point: Google's AI Overviews.
What to Optimise for LLMO
Here are key things to look out for when optimising for Large Language Models:
1. Entity Mentions Over Backlinks:
Focus on appearing in podcasts, research papers, and news articles. Prioritise brand and topic mentions in authoritative spaces, not just links.
2. Structured Data Everywhere:
Implement schema for products, articles, FAQs, and reviews. Use JSON-LD to signal E-E-A-T elements: authorship, reviews, and experience claims.
3. Prompt-Tested Content:
Regularly test your visibility by prompting ChatGPT, Gemini, and Claude with buyer queries. Track how your brand is described, which content gets cited, and whether you're summarised or skipped.
4. Answer-Ready Formatting:
Break content into digestible, retrievable chunks. Use questions, bullet points, short summaries, and explainer blocks.
5. API and Agent Compatibility:
Expose clean product or service metadata. Create open APIs or structured endpoints for agent transactions.
Pro Tip: Use Langfuse to map co-occurrence density across prompts, like analyzing which brand or product terms co-occur most frequently in LLM outputs. And PromptLayer to simulate zero-click prompt paths. This lets you understand how LLMs chain context, ensuring your content surfaces across varied user queries.
Here's how:
Export logs from AI prompt tests and apply Langfuse's co-reference visualization to identify high-impact entity associations. Then, use PromptLayer to simulate user queries that don’t require clicks (e.g., “best productivity software for remote teams”) to trace how LLMs chain information.
If your brand doesn't appear or gets cited without core context, optimize content to tighten those entity relationships.
Combining Langfuse's prompt log insights with PromptLayer's chain modelling can help you reverse-engineer how content spreads across AI conversations and then reinforce those pathways with targeted schema and structured data.
Case in Point: Google's AI Overviews.
What to Optimise for LLMO
Here are key things to look out for when optimising for Large Language Models:
1. Entity Mentions Over Backlinks:
Focus on appearing in podcasts, research papers, and news articles. Prioritise brand and topic mentions in authoritative spaces, not just links.
2. Structured Data Everywhere:
Implement schema for products, articles, FAQs, and reviews. Use JSON-LD to signal E-E-A-T elements: authorship, reviews, and experience claims.
3. Prompt-Tested Content:
Regularly test your visibility by prompting ChatGPT, Gemini, and Claude with buyer queries. Track how your brand is described, which content gets cited, and whether you're summarised or skipped.
4. Answer-Ready Formatting:
Break content into digestible, retrievable chunks. Use questions, bullet points, short summaries, and explainer blocks.
5. API and Agent Compatibility:
Expose clean product or service metadata. Create open APIs or structured endpoints for agent transactions.
Pro Tip: Use Langfuse to map co-occurrence density across prompts, like analyzing which brand or product terms co-occur most frequently in LLM outputs. And PromptLayer to simulate zero-click prompt paths. This lets you understand how LLMs chain context, ensuring your content surfaces across varied user queries.
Here's how:
Export logs from AI prompt tests and apply Langfuse's co-reference visualization to identify high-impact entity associations. Then, use PromptLayer to simulate user queries that don’t require clicks (e.g., “best productivity software for remote teams”) to trace how LLMs chain information.
If your brand doesn't appear or gets cited without core context, optimize content to tighten those entity relationships.
Combining Langfuse's prompt log insights with PromptLayer's chain modelling can help you reverse-engineer how content spreads across AI conversations and then reinforce those pathways with targeted schema and structured data.
Case in Point: Google's AI Overviews.
Mainstream media is taking notice too.
Mainstream media is taking notice too.
A recent article from the New York Post reported that publishers are seeing traffic declines due to Google’s AI-generated summaries, with some media executives warning that AI Overviews could "devastate" ad revenue by reducing clicks and time-on-site sources.
A study by Semrush and Datos found that over 88.1% of informational queries on Google now trigger AI Overviews Sites not structured for summarisation or schema-rich delivery are effectively skipped.
To stay visible:
Use FAQPage, SpeakableSpecification, and HowTo schema types
Ensure clear author attribution and content signatures
Align metadata with what AI parses as helpful, trustworthy, and experience-based
Google AI Overview inclusion is highly correlated with schema-marked definitions and answer fragments (30–50 words in length). Short, authoritative answers outperform long-form explanations.
A recent article from the New York Post reported that publishers are seeing traffic declines due to Google’s AI-generated summaries, with some media executives warning that AI Overviews could "devastate" ad revenue by reducing clicks and time-on-site sources.
A study by Semrush and Datos found that over 88.1% of informational queries on Google now trigger AI Overviews Sites not structured for summarisation or schema-rich delivery are effectively skipped.
To stay visible:
Use FAQPage, SpeakableSpecification, and HowTo schema types
Ensure clear author attribution and content signatures
Align metadata with what AI parses as helpful, trustworthy, and experience-based
Google AI Overview inclusion is highly correlated with schema-marked definitions and answer fragments (30–50 words in length). Short, authoritative answers outperform long-form explanations.
A recent article from the New York Post reported that publishers are seeing traffic declines due to Google’s AI-generated summaries, with some media executives warning that AI Overviews could "devastate" ad revenue by reducing clicks and time-on-site sources.
A study by Semrush and Datos found that over 88.1% of informational queries on Google now trigger AI Overviews Sites not structured for summarisation or schema-rich delivery are effectively skipped.
To stay visible:
Use FAQPage, SpeakableSpecification, and HowTo schema types
Ensure clear author attribution and content signatures
Align metadata with what AI parses as helpful, trustworthy, and experience-based
Google AI Overview inclusion is highly correlated with schema-marked definitions and answer fragments (30–50 words in length). Short, authoritative answers outperform long-form explanations.
A recent article from the New York Post reported that publishers are seeing traffic declines due to Google’s AI-generated summaries, with some media executives warning that AI Overviews could "devastate" ad revenue by reducing clicks and time-on-site sources.
A study by Semrush and Datos found that over 88.1% of informational queries on Google now trigger AI Overviews Sites not structured for summarisation or schema-rich delivery are effectively skipped.
To stay visible:
Use FAQPage, SpeakableSpecification, and HowTo schema types
Ensure clear author attribution and content signatures
Align metadata with what AI parses as helpful, trustworthy, and experience-based
Google AI Overview inclusion is highly correlated with schema-marked definitions and answer fragments (30–50 words in length). Short, authoritative answers outperform long-form explanations.
LLMO + SEO = Future-Proof Visibility
LLMO + SEO = Future-Proof Visibility
You don’t need to abandon SEO. But without LLMO, you’re not competing in the spaces where users are discovering answers. Combining both strategies ensures you’re present in:
Classic search listings
AI-generated answers
Voice queries
Autonomous agent transactions
Next Steps: Run an LLMO Visibility Audit
Follow these steps to run a quick LLMO visibility audit for your brand:
Prompt ChatGPT, Gemini, Claude: What happens when you prompt them with buyer-aligned queries?
Check Citations: Who do they quote, link to, or summarise?
Reverse-Engineer Your Presence: Use schema, content restructuring, and earned media to increase your retrieval score.
Feed your most important pages into embedding-friendly platforms like Weaviate or Supabase Vector Store to increase vector retrievability for private LLMs.
You don’t need to abandon SEO. But without LLMO, you’re not competing in the spaces where users are discovering answers. Combining both strategies ensures you’re present in:
Classic search listings
AI-generated answers
Voice queries
Autonomous agent transactions
Next Steps: Run an LLMO Visibility Audit
Follow these steps to run a quick LLMO visibility audit for your brand:
Prompt ChatGPT, Gemini, Claude: What happens when you prompt them with buyer-aligned queries?
Check Citations: Who do they quote, link to, or summarise?
Reverse-Engineer Your Presence: Use schema, content restructuring, and earned media to increase your retrieval score.
Feed your most important pages into embedding-friendly platforms like Weaviate or Supabase Vector Store to increase vector retrievability for private LLMs.
You don’t need to abandon SEO. But without LLMO, you’re not competing in the spaces where users are discovering answers. Combining both strategies ensures you’re present in:
Classic search listings
AI-generated answers
Voice queries
Autonomous agent transactions
Next Steps: Run an LLMO Visibility Audit
Follow these steps to run a quick LLMO visibility audit for your brand:
Prompt ChatGPT, Gemini, Claude: What happens when you prompt them with buyer-aligned queries?
Check Citations: Who do they quote, link to, or summarise?
Reverse-Engineer Your Presence: Use schema, content restructuring, and earned media to increase your retrieval score.
Feed your most important pages into embedding-friendly platforms like Weaviate or Supabase Vector Store to increase vector retrievability for private LLMs.
You don’t need to abandon SEO. But without LLMO, you’re not competing in the spaces where users are discovering answers. Combining both strategies ensures you’re present in:
Classic search listings
AI-generated answers
Voice queries
Autonomous agent transactions
Next Steps: Run an LLMO Visibility Audit
Follow these steps to run a quick LLMO visibility audit for your brand:
Prompt ChatGPT, Gemini, Claude: What happens when you prompt them with buyer-aligned queries?
Check Citations: Who do they quote, link to, or summarise?
Reverse-Engineer Your Presence: Use schema, content restructuring, and earned media to increase your retrieval score.
Feed your most important pages into embedding-friendly platforms like Weaviate or Supabase Vector Store to increase vector retrievability for private LLMs.
How to Implement It:
How to Implement It:
Chunk and Clean Content: Break each key page into semantic blocks (100–300 words each). Remove HTML clutter; keep plain, informative text.
Generate Embeddings: Use OpenAI’s text-embedding-3-small or Cohere’s embedding models. Submit each chunk to the embedding API to get vector representations.
Choose a Vector Store: Use Weaviate for enterprise needs and Supabase for lightweight projects.
Store with Metadata: Save embeddings with labels (title, URL, section summary) for retrieval.
Enable Vector Search: Use cosine similarity to retrieve the most relevant chunk in response to queries.
Monitor & Iterate: Update your stored chunks when content changes. Track vector hits using Langfuse or OpenLLMetry.
FAQs
What is the main difference between SEO and LLMO?
SEO focuses on optimizing for search engine rankings, while LLMO is about making your content retrievable and citable by large language models like ChatGPT and Gemini. LLMO emphasizes structured data, entity mentions, and answer-ready formatting.
How can I tell if my brand is visible in AI answers?
Prompt tools like ChatGPT or Perplexity with customer-aligned queries. If your brand isn’t cited or summarized, it's a signal you need to enhance LLM visibility using schema, co-occurrence strategies, and embedding.
What tools help with LLMO optimisation?
Langfuse for tracking co-occurrence patterns, PromptLayer for prompt chain simulation, and vector databases like Weaviate or Supabase for embedding-based retrievability.
Does Google still matter if AI engines dominate visibility?
Absolutely. Google AI Overviews integrate LLMs, and traditional SEO still complements LLMO. The two strategies now work in tandem—SEO fuels crawlability and rankings, while LLMO ensures you’re the answer in AI-generated content.
LLMO vs SEO: Final Thought
Traffic hasn’t disappeared. It’s moved—into AI layers where mentions, metadata, and meaning win. LLMO is how you follow it. Action the tips here to start surfacing in AI answers.
Or, reach out to us at LangSync for a custom LLMO visibility roadmap.
Chunk and Clean Content: Break each key page into semantic blocks (100–300 words each). Remove HTML clutter; keep plain, informative text.
Generate Embeddings: Use OpenAI’s text-embedding-3-small or Cohere’s embedding models. Submit each chunk to the embedding API to get vector representations.
Choose a Vector Store: Use Weaviate for enterprise needs and Supabase for lightweight projects.
Store with Metadata: Save embeddings with labels (title, URL, section summary) for retrieval.
Enable Vector Search: Use cosine similarity to retrieve the most relevant chunk in response to queries.
Monitor & Iterate: Update your stored chunks when content changes. Track vector hits using Langfuse or OpenLLMetry.
FAQs
What is the main difference between SEO and LLMO?
SEO focuses on optimizing for search engine rankings, while LLMO is about making your content retrievable and citable by large language models like ChatGPT and Gemini. LLMO emphasizes structured data, entity mentions, and answer-ready formatting.
How can I tell if my brand is visible in AI answers?
Prompt tools like ChatGPT or Perplexity with customer-aligned queries. If your brand isn’t cited or summarized, it's a signal you need to enhance LLM visibility using schema, co-occurrence strategies, and embedding.
What tools help with LLMO optimisation?
Langfuse for tracking co-occurrence patterns, PromptLayer for prompt chain simulation, and vector databases like Weaviate or Supabase for embedding-based retrievability.
Does Google still matter if AI engines dominate visibility?
Absolutely. Google AI Overviews integrate LLMs, and traditional SEO still complements LLMO. The two strategies now work in tandem—SEO fuels crawlability and rankings, while LLMO ensures you’re the answer in AI-generated content.
LLMO vs SEO: Final Thought
Traffic hasn’t disappeared. It’s moved—into AI layers where mentions, metadata, and meaning win. LLMO is how you follow it. Action the tips here to start surfacing in AI answers.
Or, reach out to us at LangSync for a custom LLMO visibility roadmap.
Chunk and Clean Content: Break each key page into semantic blocks (100–300 words each). Remove HTML clutter; keep plain, informative text.
Generate Embeddings: Use OpenAI’s text-embedding-3-small or Cohere’s embedding models. Submit each chunk to the embedding API to get vector representations.
Choose a Vector Store: Use Weaviate for enterprise needs and Supabase for lightweight projects.
Store with Metadata: Save embeddings with labels (title, URL, section summary) for retrieval.
Enable Vector Search: Use cosine similarity to retrieve the most relevant chunk in response to queries.
Monitor & Iterate: Update your stored chunks when content changes. Track vector hits using Langfuse or OpenLLMetry.
FAQs
What is the main difference between SEO and LLMO?
SEO focuses on optimizing for search engine rankings, while LLMO is about making your content retrievable and citable by large language models like ChatGPT and Gemini. LLMO emphasizes structured data, entity mentions, and answer-ready formatting.
How can I tell if my brand is visible in AI answers?
Prompt tools like ChatGPT or Perplexity with customer-aligned queries. If your brand isn’t cited or summarized, it's a signal you need to enhance LLM visibility using schema, co-occurrence strategies, and embedding.
What tools help with LLMO optimisation?
Langfuse for tracking co-occurrence patterns, PromptLayer for prompt chain simulation, and vector databases like Weaviate or Supabase for embedding-based retrievability.
Does Google still matter if AI engines dominate visibility?
Absolutely. Google AI Overviews integrate LLMs, and traditional SEO still complements LLMO. The two strategies now work in tandem—SEO fuels crawlability and rankings, while LLMO ensures you’re the answer in AI-generated content.
LLMO vs SEO: Final Thought
Traffic hasn’t disappeared. It’s moved—into AI layers where mentions, metadata, and meaning win. LLMO is how you follow it. Action the tips here to start surfacing in AI answers.
Or, reach out to us at LangSync for a custom LLMO visibility roadmap.
Chunk and Clean Content: Break each key page into semantic blocks (100–300 words each). Remove HTML clutter; keep plain, informative text.
Generate Embeddings: Use OpenAI’s text-embedding-3-small or Cohere’s embedding models. Submit each chunk to the embedding API to get vector representations.
Choose a Vector Store: Use Weaviate for enterprise needs and Supabase for lightweight projects.
Store with Metadata: Save embeddings with labels (title, URL, section summary) for retrieval.
Enable Vector Search: Use cosine similarity to retrieve the most relevant chunk in response to queries.
Monitor & Iterate: Update your stored chunks when content changes. Track vector hits using Langfuse or OpenLLMetry.
FAQs
What is the main difference between SEO and LLMO?
SEO focuses on optimizing for search engine rankings, while LLMO is about making your content retrievable and citable by large language models like ChatGPT and Gemini. LLMO emphasizes structured data, entity mentions, and answer-ready formatting.
How can I tell if my brand is visible in AI answers?
Prompt tools like ChatGPT or Perplexity with customer-aligned queries. If your brand isn’t cited or summarized, it's a signal you need to enhance LLM visibility using schema, co-occurrence strategies, and embedding.
What tools help with LLMO optimisation?
Langfuse for tracking co-occurrence patterns, PromptLayer for prompt chain simulation, and vector databases like Weaviate or Supabase for embedding-based retrievability.
Does Google still matter if AI engines dominate visibility?
Absolutely. Google AI Overviews integrate LLMs, and traditional SEO still complements LLMO. The two strategies now work in tandem—SEO fuels crawlability and rankings, while LLMO ensures you’re the answer in AI-generated content.
LLMO vs SEO: Final Thought
Traffic hasn’t disappeared. It’s moved—into AI layers where mentions, metadata, and meaning win. LLMO is how you follow it. Action the tips here to start surfacing in AI answers.
Or, reach out to us at LangSync for a custom LLMO visibility roadmap.
