LLM Optimization (LLMO) improves how large language models such as those behind ChatGPT, Gemini, Claude, Copilot, and Perplexity understand and describe your brand, by strengthening the public, crawlable information those models learn from and retrieve. It is for businesses that AI assistants describe incorrectly, confuse with someone else, or omit entirely. Nobody can edit a model’s training data; what can be controlled is the accuracy, consistency, authority, and freshness of what the web says about you. Fix Website Issues handles that as a technical SEO and website repair team, within a broader AI visibility services practice.
Honest About What Can Be Controlled | Entity and Accuracy Focused | Houston-Based, Nationwide | Since 2013
Accuracy, completeness, and consistency of what AI assistants say about the brand.
ChatGPT, Gemini, Claude, Copilot, Perplexity, and whatever comes next.
Website, profiles, directories, references, schema, and freshness. Nothing inside the model.
Prompt-set accuracy logs, Bing AI data, referral analytics. Observations labeled as such.
LLM Optimization is the practice of improving how large language models understand, describe, and surface a brand by strengthening the accuracy, consistency, authority, and freshness of the public information those models learn from during training and retrieve from the web when answering. It is the foundation beneath ChatGPT SEO, GEO, and AEO: before any of those can work, a model has to know who you are and get it right.
A language model learns about a brand in two ways. During training, it absorbs whatever the public web said about the brand up to a cutoff date. During use, many assistants also retrieve live pages to ground their answers. The first cannot be edited by anyone outside the model provider. The second depends entirely on what is crawlable and clear right now. LLM Optimization works on the second and, because today’s web becomes tomorrow’s training data, gradually influences the first.
We are deliberately careful with claims here. No agency can inject a brand into a proprietary training set, control what a model “believes,” or guarantee how it answers. What can be done is make the public record of the business accurate, consistent, well-corroborated, and easy for crawlers to read, so that any model, current or future, that looks for the brand finds one clear story.
Old addresses on abandoned profiles, a legal name that differs from the trade name, a former product line still described on third-party sites, a merger nobody announced clearly, or simply very little public information at all. Models resolve ambiguity by picking whatever is most repeated, which is often the outdated version. Correcting that is entity work: one canonical set of facts, stated the same way everywhere a crawler looks.
The businesses that need this most are the ones whose public record is thin, inconsistent, or out of date, because those are the conditions under which a model guesses.
Businesses that AI assistants describe incorrectly. Wrong location, wrong services, a former owner, a brand confused with a similarly named company elsewhere. The fix is on the web, and it is entity work.
Rebranded, merged, or relocated companies. Models carry the old story until the new one is stated consistently everywhere and corroborated by outside sources.
Healthcare groups and professional firms with many named people. Providers and attorneys are entities too. When their names, credentials, and affiliations differ across the site, directories, and licensing boards, models struggle to connect them to the practice.
Companies with strong products and almost no public footprint. A great business that nobody writes about gives a model nothing to learn. Publishing original, specific content and earning references is the only way in.
Brands that compete with a larger namesake. Disambiguation through consistent descriptors, schema, and references keeps a model from merging two entities into one.
A site that blocks crawlers, renders its text only through scripts, or is recovering from a hack is repaired first; our website repair and SEO specialists handle that. If you already know the entity is clean and want citations, GEO is the next layer. If you have no baseline, the AI Visibility Audit comes first.
Each of these is an information problem on the public web, which is why each one has a concrete fix.
Old address, discontinued service, former name. Fix: find every public source repeating the old fact, correct or remove it, publish the current fact with a date, and corroborate it.
A similar name elsewhere gets merged with yours. Fix: consistent disambiguating descriptors, Organization schema with sameAs to official profiles, and references that tie the name to your location and services.
Little public information, so assistants have nothing to say. Fix: an About page that states the facts plainly, original content that demonstrates expertise, and listings and references in the sources models draw on.
The site has the right facts but blocks AI crawlers or hides text behind scripts, so models only see stale third-party data. Fix: crawler access, server-rendered text, and index coverage on Google and Bing.
Providers, partners, or authors are not linked to the company anywhere a crawler can see. Fix: team pages, Person schema with worksFor, and consistent names across licensing boards, directories, and profiles.
Fixes are made but nobody checks the models. Fix: a brand-description prompt set run monthly across assistants, with accuracy scored against the canonical facts.
The work is organized around the controllable areas. Technical items are implemented by the team that performs WordPress repair and SEO repair, so crawler and rendering fixes are done, not delegated.
The evaluation starts by writing down what is true, then finds every place the web disagrees. The output is a canonical fact sheet, an accuracy scorecard by assistant, and a correction roadmap.
The canonical fact sheet is written with the client and becomes the reference for everything published and every correction requested.
Unblock crawlers, fix rendering, confirm indexing, and make sure the About, team, and service pages state the facts in plain HTML text.
Update or remove stale data on profiles, directories, boards, and third-party pages, prioritized by how often assistants repeat the error.
Add Organization, LocalBusiness, and Person schema that mirrors the visible content, with sameAs and worksFor links so people and brand resolve to one entity.
Original content in your own words about how you work, what you have observed, and what you recommend. This is what gives a model something distinctive to learn.
Pursue listings and mentions in the sources models rely on, date updates, and retire pages that no longer reflect reality.
The brand-description prompt set is re-run monthly and scored. Persistent errors are traced back to remaining sources and corrected.
We do not claim to reach inside the model. Everything on this page is about the public web, because that is the only thing anyone outside the model provider can change. Any agency describing “training data optimization” as a service is describing something it cannot do.
Errors have sources. When an assistant states a wrong fact, we treat it as a lead: something on the web is saying it. Finding and fixing that source is more productive than publishing a rebuttal.
Crawler access comes first. A frequent pattern from our repair work: the site was corrected years ago, but a firewall rule blocks every crawler except Google, so models keep learning from stale directory pages instead. The fix takes minutes and precedes all other work.
Freshness is a signal we control. Dated updates, retired pages, and a current About page cost little and reduce the chance that a model prefers an older, more-repeated version of the story.
SEO makes pages rank; LLM Optimization makes a brand understood. One is about pages and queries, the other about entities and facts.
| Area | SEO | LLM Optimization |
|---|---|---|
| Primary purpose | Rank pages for queries | Have language models understand and describe the brand accurately |
| Unit of work | Page and keyword | Entity and fact |
| Where it matters | Search result pages | Any assistant answer that mentions or describes the brand, now and in future models |
| Typical work | Technical fixes, keyword pages, links | Crawler access, fact sheet, corrections across the web, entity schema, original content, references, freshness |
| Measurement | Rankings, clicks | Accuracy of assistant descriptions over time, Bing AI data, referrals |
| Limitations | Rankings do not fix a misunderstood brand | Cannot edit training data or control outputs; effects on future models are gradual |
Situation. A commercial HVAC company changed its name after a merger and moved its headquarters. Two years later, assistants still describe it under the old name at the old address, and one confuses it with an unrelated residential company in another state that shares part of the new name.
Likely findings. The old brand persists on forty-plus directory and association listings; the new site blocks non-Google crawlers; the About page says nothing about the merger; no Organization schema; team members are listed under the old company on LinkedIn and licensing boards.
Sequence. Fact sheet and crawler access (week 1). About page rewritten to state the merger, both names, the move, and the service area, dated (week 1). Corrections submitted to every listing, prioritized by which ones assistants cite (weeks 2 to 6). Organization schema with alternateName and sameAs; Person schema with worksFor for leadership (week 2). Distinctive descriptors used consistently to separate the company from its namesake. Two industry-association profiles updated to reference the merger (ongoing).
Measurement. Monthly brand-description prompt set scored for accuracy across five assistants. Live retrieval answers typically correct sooner; training-based answers change only with model updates. Structure, not results; outcomes are not guaranteed.
We can run the brand-description tests across five assistants, score them against the facts, and trace every error to its source before recommending anything.
Fix Website Issues is the repair and technical SEO division of WevTEC, Houston-based and serving businesses nationwide since 2013. LLM Optimization is delivered by the same team that diagnoses crawl failures, migrations, and hacked sites, which is why access and rendering are checked before any correction campaign begins.
Strategy and implementation are handled by the Fix Website Issues technical SEO team under WevTEC. This page was last updated September 2026. More about the team.
LLM Optimization is delivered remotely from Houston, Texas, to clients in Houston, Dallas, Austin, and San Antonio; Miami and South Florida; Los Angeles, San Diego, and San Francisco; and across the U.S. Houston is our only physical office.
Related: AI Search Optimization · ChatGPT SEO · Generative Engine Optimization · AI Visibility Audit
Careful answers to a topic that attracts careless claims.
LLM Optimization is the practice of improving how large language models understand, describe, and surface a brand by strengthening the accuracy, consistency, authority, and freshness of the public information those models learn from and retrieve from the web.
No. Training data is controlled by the model provider. LLM Optimization works only on the public web, which influences live retrieval immediately and future training gradually. Any service claiming to edit training data is overstating what is possible.
Through training, which reflects the public web up to a cutoff date, and through live retrieval, where many assistants fetch current pages to answer. Only the second can be influenced directly, by making current, accurate, corroborated information crawlable.
Usually because the wrong fact is repeated on more public sources than the right one: old directory listings, stale profiles, or third-party pages. Models resolve conflicts toward what is most repeated. The fix is to correct those sources and corroborate the current facts.
Accurate Organization, LocalBusiness, and Person schema helps crawlers connect names, places, and people to one entity. It supports understanding; it does not cause a model to say anything, and it must match the visible content.
No. No agency controls model outputs, and mentions vary by prompt, user, and model version. We score accuracy against an agreed fact sheet each month and report changes; we do not promise descriptions or mentions.
Live-retrieval answers can improve within weeks of corrections being crawled. Answers that depend on training data change only when the provider updates the model, on a schedule that is not public. Both are estimates, not guarantees.
LLM Optimization is the entity foundation: making sure models know who you are and get the facts right. GEO builds on that to earn citations, and ChatGPT SEO applies both to one platform.
That is a decision about contributing to training. Blocking training crawlers such as GPTBot does not remove you from ChatGPT search, and allowing them does not guarantee inclusion. Search and user-triggered crawlers are usually worth allowing if you want to be found.
With the AI Visibility Audit, which includes the brand-description accuracy benchmark and entity consistency review that LLM Optimization is built on.
Request an AI Visibility Audit and receive a brand accuracy scorecard across five assistants, a trace of where each error comes from, and a correction roadmap. No deposit, no obligation.