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B2B LLMO services for AI and SaaS companies

We rank you in Google for the terms your buyers use, then extend the same content into AI answers, so you are found where enterprise buyers actually search now.

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15+ YEARS CREATING WITH BRANDS YOU ♥︎

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Our B2B LLMO services

Large language model optimization (LLMO) is the practice of building your brand’s presence in the data that large language models train on, so ChatGPT, Claude, Gemini, and Perplexity name you from memory when buyers ask who does what. MQL Magnet runs LLMO as the fourth engine in the Engine Optimization Matrix. Where GEO wins live retrieval, our llm optimization services win the model’s baked in knowledge.

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Model Presence
Audit

We prompt ChatGPT, Claude, Gemini, and Perplexity the way your buyers do and document your industry share of voice.

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Mention Density Campaigns

We build steady mentions across high trust domains – the signal models weight when deciding brands to name.

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Training Data Footprint

We expand your presence across the public web, archives, and syndication likely to feed the next training run.

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Digital PR and Syndication

We place your ideas in the publications and platforms whose content reliably ends up in training corpora.

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Distinctive POV and Named IP

We develop the frameworks, terms, and citable phrasing that models learn to associate with your brand.

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Brand Association Shaping

We shape the specific phrases and categories models attach to your brand, so your model highlight is the one you want.

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Author Entity Building

We connect your executives to their work through sameAs links, bylines, and profiles so models attribute expertise.

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LLM Visibility Measurement

We run structured prompt testing and server log analysis to separate training presence from live retrieval.

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The framework behind our success

Every engagement runs on the Engine Optimization Matrix, our framework for digital visibility. It maps four engines, SEO, AEO, GEO, and LLMO, against five levers, Message, Schema, Authority, Distribution, and Citation. LLMO owns the fourth row. Its cells cover distinctive POV content with citable phrasing, author entity links, training data presence across the public web and archives, mention density on high trust domains over time, and whether ChatGPT, Claude, or Perplexity name you specifically when prompted. 

Engine Optimization Matrix from MQL Magnet
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Why partner with MQL Magnet

LLMO is the engine most agencies cannot even describe, which is exactly why I built a practice around it. When a model answers from its training rather than a live search, there is no ranking to win and no page to cite. There's only whether the model learned your name. And it's a slow, compounding contest decided by what exists about you on the public web over time.

Get a deeper grip on this digital visibility engine in this Ultimate LLMO Guide.

See how the method works across engines in the Engine Optimization Matrix

Pair strategy with execution with our B2B Content Development Services

Get your content seen with our Content Distribution Services

Frequently asked questions (FAQs) 

Here are 6 considerations to include in your evaluation.

What is large language model optimization (LLMO)?

Large language model optimization is the practice of building your brand’s presence in the data that models like ChatGPT, Claude, and Gemini train on, so they name your brand from memory when buyers ask. 

How is LLMO different from generative engine optimization?

GEO optimizes for live retrieval, where an engine searches the web and cites sources in real time. LLMO optimizes for training presence, where the model answers from what it already learned. 

Can you really influence what an LLM says about our brand?

Yes, within honest limits. Models learn from the public web, so consistent mentions on high trust domains, distinctive named frameworks, clean entity signals, and syndicated authorship shift whether and how models name a brand.

How long before LLMO produces results?

LLMO is the slowest engine by design, because gains land when models retrain or refresh their knowledge. Retrieval assisted surfaces reflect the work in weeks, while pure training presence typically shifts over quarters. 

What does an LLMO engagement include?

A model presence audit, training data footprint expansion, distinctive POV and named IP development, author entity building, mention density campaigns, and structured prompt testing that measures model recall over time.

How do you measure language model optimization (LLMO)?

We run a fixed panel of buyer prompts across models on a set cadence and score whether you are named, how you are described, and who you are named alongside. We pair that with server log analysis.

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