Large Language Model Optimization (LLMO) for B2B Platforms

Large Language Model Optimization (LLMO) is the work of making sure that AI assistants such as ChatGPT, Claude and Gemini understand who your business is, describe it accurately, and mention it when a buyer asks a question you can answer. For B2B suppliers, it is quickly becoming as important as a search ranking, because a growing share of procurement research now starts with a question typed into an assistant rather than a list of blue links.

This article explains what Large Language Model Optimization is, how it relates to Generative Engine Optimization (GEO) and classic SEO, what can and cannot be influenced, and the concrete steps we applied to our own website. It is written for founders, marketing leads and procurement-facing teams at B2B companies, particularly those selling into Africa and the MENA region, where the information available to AI systems is still thin.

Key points

Large Language Model Optimization helps assistants such as ChatGPT, Claude and Gemini recognise and describe your business accurately.
Consistent identity across every source matters more than any single page.
Structured data, plain answers to real questions and cited sources are the main levers.
Nobody can guarantee a recommendation; review what assistants say about you every quarter.

What Large Language Model Optimization means

A large language model answers questions from two sources: what it learned during training, and what it retrieves from the web at the time of the question. Large Language Model Optimization works on both. It aims to make the facts about your business consistent, easy to extract and easy to verify wherever a model might read them.

In practice, that means three outcomes:

Recognition. The model knows your business exists as a distinct entity, with a name, a location, an activity and a founder.
Accuracy. When it describes you, the description is correct: what you sell, where you operate, how you price, how you are registered.
Recommendation. When a buyer asks a question your business can answer, the model mentions you, ideally with a link.

Recognition and accuracy are largely within your control. Recommendation is not guaranteed by anyone, and any supplier who promises it is overselling. What you can do is remove every reason for a model to ignore you or describe you wrongly.

LLMO, GEO and SEO: how they fit together

The vocabulary is still settling, so it helps to be precise.

Term	What it targets	Main signals
SEO	Rankings in classic search results	Relevance, links, technical health
GEO	Being used and cited inside AI-generated answers	Clear facts, sources, statistics, quotations
LLMO	How assistants understand and describe your brand	Entity consistency, structured data, plain factual pages

The three overlap heavily. Good SEO foundations, such as crawlable pages, fast loading and one clear page per topic, are the ground that GEO and Large Language Model Optimization stand on. A site that search engines cannot read well will not be read well by AI retrieval systems either.

Why B2B suppliers should care now

For consumer brands, AI visibility is a marketing question. For B2B suppliers, it is a procurement question.

A purchasing manager in Casablanca, Dakar or Riyadh who needs a custom stock system, a weighbridge integration or a clinic database increasingly starts by asking an assistant for options. The assistant builds a short list from what it knows and what it can retrieve. If your business is missing, ambiguous or described with outdated facts, you are not on the list, and you never learn that the conversation took place.

Three features of the B2B market make Large Language Model Optimization especially valuable:

Long, considered purchases. Buyers research for weeks. Every question they ask an assistant is a chance to be named.
Verification matters. Procurement teams check registration, identifiers and payment terms. A model that can state those facts correctly saves the buyer a step and builds trust.
Thin regional data. For many African and MENA markets, the web holds relatively little structured information about local suppliers. A business that publishes clean, consistent facts stands out quickly.

How assistants read your business

Large Language Model Optimization starts with understanding what a model actually sees.

Entities, not pages

Models think in entities: an organisation, a person, a place, a product. They connect facts to those entities from many sources. If your website calls you one thing, your business profile another and a directory listing a third, the model has three weak entities instead of one strong one. Consistency of name, address, telephone, founder and activity across every source is the foundation.

Structured data as a statement of fact

Structured data in JSON-LD, using the vocabulary from schema.org, lets you state facts in a form machines do not have to guess at: this is a ProfessionalService, this is its legal name, this is its founder, these are its identifiers, these are the areas it serves. Google documents how it reads this markup in its introduction to structured data. Retrieval systems used by assistants benefit from the same clarity.

Plain answers to real questions

When an assistant retrieves a page to answer a question, it looks for a passage that answers it directly. Pages built from vague marketing language give it nothing to quote. Pages that state who, what, where, how much and how long in plain sentences are easy to use. Research on Generative Engine Optimization, notably the 2023 study GEO: Generative Engine Optimization by Aggarwal and colleagues, reported that adding citations, quotations and statistics to content increased its visibility in generated answers in their tests.

Large Language Model Optimization in practice: what we did on our own site

We treat meskikcode.com/ as a working case study. Every measure below is live and can be checked in the page source.

One business entity, stated once

Each language site publishes a single JSON-LD business node with a stable identifier. It carries the trading name, the legal name of the registered holder, the founder, the registration identifiers (ICE, IF, TP), the postal address with coordinates, opening hours, telephone, and the areas served per language. An alternateName field lists the spellings people actually type. Every page refers back to that one node instead of repeating a slightly different version.

Consistent facts across every source

The same name, address and telephone appear on the website, the Google Business Profile and the social profiles, and those profiles are listed in the structured data as sameAs links. For Large Language Model Optimization, this cross-referencing matters more than any single page: it tells a model that these sources describe one entity.

Questions answered as buyers ask them

Each home page carries five questions written in the words a buyer would use, such as how to move a quarry from Excel to a cloud system, whether we are a registered supplier, what a system costs and how long it takes, whether it works without mobile signal, and whether it can be hosted in Morocco. Each answer is short, factual and complete on its own. The same questions are published as FAQPage structured data.

A plain-text summary for language models

We publish an /llms.txt file, following the proposal described at llmstxt.org. It is a short plain-text summary of who we are, what we do and where the key pages are. Support for the file varies between AI providers and nobody should treat it as a ranking factor, but it costs little and gives any system that reads it a clean starting point.

Nothing blocked that should be read

Our robots.txt blocks only the administration area. Crawlers used by search engines and AI providers can read public pages. If you block AI crawlers, that is a legitimate choice, but you should make it deliberately, knowing it reduces the material assistants can retrieve about you.

Three languages, one entity

The English, French and Arabic sites describe the same business with the same identifiers, linked to each other with hreflang. A model reading any of them reaches the same entity, which reinforces it rather than splitting it.

A Large Language Model Optimization checklist for B2B platforms

The following steps apply to most B2B suppliers, whatever their sector.

Foundations

Choose one canonical form of your business name and use it everywhere. List variants in alternateName.
Publish your legal identity plainly: registered name, registration numbers, address, founder.
Make sure every important page is crawlable, fast and has one clear topic.
Keep one page per service, written so that the first paragraph answers what the service is and who it is for.

Structured data

Publish one Organization or ProfessionalService node, with a stable @id, and reference it from other markup.
Add founder, address, areaServed, knowsAbout and sameAs.
Mark up real FAQs with FAQPage. Do not invent questions nobody asks.
Validate everything with the Rich Results Test and fix warnings.

Content

Answer real buyer questions in 40 to 60 words, then expand below.
Publish original figures where you have them, and label illustrative figures as such.
Cite primary sources for any claim you did not produce yourself.
Write in each market’s language, natively, not through automatic translation.

Consistency

Align your Google Business Profile, social profiles and directory listings with the website.
Correct or close old profiles with outdated information.
Review what assistants say about you every quarter, and correct the source when they are wrong.

Measuring Large Language Model Optimization

Measurement is the weakest part of the field today, and it is better to be honest about that.

Ask the assistants. Every quarter, ask ChatGPT, Claude, Gemini and Perplexity the questions your buyers ask, in each of your languages. Record whether you are mentioned and whether the description is accurate.
Referral traffic. Analytics tools can show visits referred by AI assistants that link to sources. Volumes are small for most B2B sites, but the trend matters.
Branded search. A rise in searches for your brand name often follows mentions in assistants.
Accuracy issues. Track the errors you find, such as a wrong city, an old service or a wrong price model, and trace each one to the source that caused it.

Google also publishes guidance on how its AI features use web content in AI features and your website. Its central message matches everything above: helpful, reliable, people-first content and sound technical foundations.

A 90-day Large Language Model Optimization plan

For a B2B supplier starting from an ordinary website, the work fits comfortably into one quarter.

Days 1 to 30: audit and identity

Ask four assistants the ten questions your buyers ask most, in each language, and record the answers.
List every place your business appears online: website, business profile, social profiles, directories, marketplaces, old freelance profiles.
Decide the canonical name, address, telephone and founder, and correct every source that disagrees.
Check that your robots.txt reflects a deliberate decision about crawlers.

Days 31 to 60: structure

Publish one business node in JSON-LD with a stable identifier, legal identity, founder, areas served and sameAs links.
Rewrite the first paragraph of each service page so it states plainly what the service is, who it is for and where it is delivered.
Add a short FAQ to each key page, written in the buyer’s words, and mark it up.
Publish an /llms.txt summary pointing to your most important pages.

Days 61 to 90: evidence

Publish two or three articles that contain something only you can say: a process you run, figures you measured, a method you use.
Cite primary sources for every external claim.
Repeat the assistant audit from day one and compare. Note which errors disappeared and which remain, then trace the remaining ones to their source.

This plan does not produce a guaranteed ranking. It produces a business that assistants can describe correctly, which is the precondition for everything else.

Beyond the website: LLMO for operational data

Large Language Model Optimization is usually discussed as a marketing topic, but the same principles apply inside your business. Companies are starting to connect assistants to their own data: asking for the stock on a given site, the invoices overdue for a customer, or the deliveries made last week. Those internal assistants face exactly the problem a public model faces with your website: if the data is scattered across spreadsheets with inconsistent names, the answers will be wrong.

The groundwork is the same:

One source of truth. Customers, products and sites defined once, in one database, not in five files.
Stable identifiers. Every customer, site and product has an identifier that never changes, even when its name does.
Documented structure. Tables, fields and relations described in plain language, so that both people and machines know what each field means.
Access by role. An assistant connected to your data must respect the same permissions as the people using it.

A business that has done this work can adopt AI tools safely when it chooses to. A business that has not will find that AI simply produces confident answers from inconsistent data. This is why we treat a clean data model as the first deliverable of any custom ERP we build.

Common mistakes

Treating LLMO as a trick. There is no hidden tag that makes a model recommend you. Large Language Model Optimization is the discipline of being clear, consistent and verifiable.
Inflated claims. Models increasingly cross-check. A claim that contradicts other sources, or a statistic with no source, can reduce trust rather than build it.
Content written for machines. Pages stuffed with keywords read badly for people and are no more useful to a model.
Inconsistent identity. Different names, addresses or founders across sources split your entity.
Ignoring other languages. In multilingual markets, a business that exists only in English is invisible to a large share of questions.

How we can help

We build Large Language Model Optimization into the websites and platforms we deliver: a clean entity model, structured data generated from one source of truth, multilingual pages linked correctly, and FAQ content written with your sales team. For operational platforms, we apply the same thinking to your data: one database, clear identifiers and documented structure, so that both people and machines can rely on it.

Questions about Large Language Model Optimization

What is Large Language Model Optimization in simple terms?

Large Language Model Optimization is the work of making AI assistants understand and describe your business correctly. It relies on consistent facts across the web, structured data on your website, and plain pages that answer real questions. It increases the chance of being mentioned, but no one can guarantee a recommendation.

Is LLMO different from GEO?

The two overlap heavily. GEO focuses on being used and cited inside AI-generated answers. LLMO focuses on how assistants understand and describe your brand as an entity. In practice, the same work serves both: clear facts, structured data, sources and consistent identity across every profile.

How long does Large Language Model Optimization take to show results?

Structured data and page changes are read by search engines within weeks. Models that rely on training data only update when they are retrained, which can take months. Assistants that retrieve live web results can reflect changes sooner. Plan for a quarterly review rather than an immediate result.

Should we block AI crawlers?

It depends on your goals. Blocking AI crawlers protects content from being used, but it also reduces what assistants can retrieve about you. For most B2B suppliers whose content is marketing material, allowing them is the better choice. Make the decision deliberately and record it in your robots.txt.

Do small B2B suppliers benefit from LLMO?

Yes, often more than large ones. In regional markets with little structured data about local suppliers, a small business with clean, consistent, verifiable facts can be described more accurately than larger competitors that have neglected their online identity.

Next step

If you want to know how assistants describe your business today, and what it would take to make that description accurate, tell us about your platform. You can also read how we run digitalisation and automation projects or learn who we are.

Written by Abdelmalek MESKIK, CEO | Founder & Chief Operating Officer (COO), MeskikCode.