🧠 LLM Optimization

Make AI Models Describe
Your Brand Correctly.

Large language models now answer millions of buying questions every day — and they describe your brand using whatever they have learned or can retrieve about you. LLM Optimization (LLMO) makes sure that picture is accurate, complete, and favourable: the right category, the right strengths, the right comparisons, and a clear reason to recommend you.

ChatGPT Perplexity Gemini Copilot Claude

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500+ brands cited across every major AI engine

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Free LLM Visibility Audit — we ask ChatGPT, Gemini, Claude, Perplexity & Copilot how they describe your brand and competitors, and show you every gap, error, and missed recommendation.

500+

Brands Cited

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Definition

What Is LLM
Optimization (LLMO)?

LLM Optimization is the practice of improving how large language models — the systems behind ChatGPT, Gemini, Claude, Copilot and Perplexity — discover, interpret, and represent your brand. Where traditional SEO asks “where do we rank?”, LLMO asks “what does the model say about us, and does it recommend us?”

Models build that answer from two places: what they learned during training (your long-term reputation across the web) and what they retrieve live when they search. LLMO works on both — strengthening the consistent, well-sourced facts about your brand across the sources models learn from, and making your own site easy for AI crawlers to retrieve, parse, and quote.

🧠

Training-Data Signals

Consistent facts about your brand across authoritative sites, directories, reviews and publications that models learn from.

🔗

Retrieval Signals

Crawlable, well-structured pages that AI search features can fetch and cite in real time.

🧬

Entity Clarity

A clearly defined brand entity — name, category, offerings, people, locations — that models cannot confuse with others.

Benefits

What You Gain From
LLM Optimization.

LLMO protects and grows the version of your brand that buyers meet inside AI assistants — before they ever visit your website.

✅

Accurate Brand Descriptions

Fewer outdated facts, wrong pricing, or confused product details in AI answers about you.

🏆

More AI Recommendations

Greater chance of being named when buyers ask “what is the best…” or “which provider should I use…” in your category.

⚖️

Fairer Competitor Comparisons

Your real differentiators show up when AI compares you side by side with competitors.

🛡️

Reputation Protection

Early detection of negative, inaccurate, or hallucinated claims so they can be corrected at the source.

📈

Higher-Intent Traffic

Visitors referred by AI assistants arrive already informed and closer to a buying decision.

🔁

Compounding Visibility

Strong entity and citation signals keep working across new models and AI features as they launch.

What Is Included

A Complete
LLMO Engagement.

Every engagement combines brand perception research, entity work, content, and technical access so models can find you, understand you, and trust you.

🔍

LLM Brand Perception Audit

We prompt every major model with real buyer questions and record how each one describes, ranks, and compares your brand.

🧬

Entity & Knowledge Graph Optimization

Organization and product entities aligned across your site, Google Knowledge Graph, Wikidata, and key directories.

📝

LLM-Friendly Content

Clear definitions, comparison pages, FAQs, and fact-dense passages that models can extract and quote accurately.

🏷️

Structured Data

Organization, Product, Service, FAQPage, and Review schema that makes your facts machine-readable.

🕷️

AI Crawler Access

Robots.txt, rendering, and llms.txt checks so GPTBot, ClaudeBot, PerplexityBot and Google-Extended can reach your key pages.

📰

Third-Party Source Building

Mentions in the publications, lists, reviews, and communities models rely on to validate brands.

🩺

Hallucination Correction

Tracing wrong AI claims back to their likely sources and fixing or outweighing them.

📊

Monthly Perception Reporting

Share of recommendation, sentiment, and accuracy tracked across models every month.

How We Work

From Misunderstood to
Recommended.

A clear four-phase plan, so you always know what is happening and why.

Phase 01 🔎

Discover

We map how each LLM currently describes you and your competitors across 50–100 buyer prompts.

Weeks 1–2

Phase 02 🧭

Define

We fix your core brand facts, entity profile, and the key messages you want models to repeat.

Weeks 2–4

Phase 03 🛠️

Optimize

Content, schema, crawler access, and third-party source work go live in priority order.

Months 2–3

Phase 04 📈

Measure & Expand

We re-test every prompt monthly, report movement, and expand to new topics and models.

Ongoing

Case Study Example

How LLMO Works
In Practice.

A typical engagement for a B2B software company, step by step.

Illustrative ExampleB2B SaaS

Correcting how AI assistants describe a growing SaaS platform

A mid-sized SaaS company noticed prospects arriving on sales calls with wrong ideas about its pricing and features — picked up from AI assistants. Competitors were being recommended for “best tool for…” prompts where the company was a strong fit.

The Challenge

▸ChatGPT and Gemini repeated an outdated pricing model
▸The brand was missing from most “best tools for…” answers
▸Competitors had stronger review and comparison coverage
▸Key product pages were blocked from some AI crawlers

Our Approach

▸Ran 80 buyer prompts across five models to set a baseline
▸Published a clear pricing page, comparison pages, and a fact-dense FAQ
▸Aligned the entity across site schema, Wikidata, G2 and Capterra
▸Opened crawler access and pitched inclusion in category round-ups

What We Measure

▸Share of recommendation across target prompts
▸Accuracy of pricing and feature descriptions
▸Sentiment of AI descriptions vs competitors
▸AI referral traffic and demo requests

This is an illustrative engagement showing how we typically run LLM Optimization. It is not a specific client result. Ask us for real, anonymised client outcomes on a call.

Measurement

Real LLMO KPIs.
No Vanity Metrics.

We report on what AI actually says about you — and what it does for your pipeline.

🎯

Share of Recommendation

How often models name you for your target buyer prompts.

✅

Description Accuracy

Percentage of AI answers that describe you correctly.

💬

Sentiment Score

Positive, neutral, or negative framing across models.

🔗

Citations to Your Site

How often AI answers link to your own pages.

📈

AI Referral Traffic

Visits from ChatGPT, Perplexity, Gemini, Copilot and others.

💼

AI-Sourced Leads

Leads and sales opportunities that started in an AI assistant.

Who It Is For

Built for Brands
Buyers Research With AI.

LLMO delivers the most value when buyers ask AI before they ever contact you.

SaaS and technology companies

Buyers ask AI to shortlist tools before booking demos.

Professional services firms

Clients ask AI who to trust for legal, financial, or consulting work.

Ecommerce and DTC brands

Shoppers ask AI which product or brand to buy.

Healthcare and wellness providers

Patients research options and providers with AI first.

Brands with reputation issues

AI repeats outdated or negative information that needs correcting.

Challenger brands

You are better than the incumbents, but AI does not know it yet.

FAQ

Frequently Asked Questions
About LLM Optimization

LLM optimization is the work of improving how large language models such as ChatGPT, Gemini, Claude, Copilot and Perplexity discover, understand, and describe your brand, so that their answers about you are accurate and you are recommended for relevant buyer questions.

The terms overlap. GEO focuses on being cited inside generative answers, AEO focuses on being the direct answer to a question, and LLMO focuses on the model’s overall understanding of your brand — its facts, category, and reputation. We run them as one integrated program.

No one can edit a model directly. What we can do is strengthen and correct the sources models learn from and retrieve, which over time changes how they describe and recommend you. We track this prompt by prompt every month.

Retrieval-based answers, such as AI search features, can change within weeks once pages are updated and crawled. Changes that depend on model training take longer and arrive as new model versions are released, so we plan for a 3–6 month horizon.

We track ChatGPT, Google Gemini and AI Overviews, Microsoft Copilot, Perplexity and Claude, and add others as they become relevant to your market.

To be cited in live AI answers, your key pages must be reachable by AI crawlers such as GPTBot, OAI-SearchBot, ClaudeBot and PerplexityBot. We review your robots.txt and help you choose what to allow based on your goals.

No honest agency can guarantee placement in AI answers. We guarantee transparent measurement and a clear, evidence-based plan, and we report exactly what changes.

Go Deeper

If AI Gets Your Brand Wrong,
Buyers Believe It.

Get a free LLM Visibility Audit. We will ask the major AI assistants how they describe your brand and competitors, and show you every error, gap, and missed recommendation — delivered in 24 hours. Prefer to talk first? Contact us.

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Make AI Models DescribeYour Brand Correctly.