LLM Optimization: Getting Your Tech Company Into ChatGPT, Claude, Gemini and Perplexity Answers

LLM optimization means getting large language models to name your company when a buyer asks. Two clocks drive it. One is what a model remembers from training, and the other is what it looks up when it searches.

Say a law firm with sixty people needs a new managed IT provider, so someone on the partner team opens four different chatbots and types the identical question into each. They get four answers that barely overlap. Nothing is broken. Really. Those are four separate systems working from four separate piles of source material.

Do the same with your own category and a notepad, and watch which names repeat. Try it today.

Each pile has three layers, and they update at very different speeds: text frozen into the model during training, an index built by the assistant’s own crawler, and pages fetched live at the moment a user asks.

I say this about sales and hiring too. AI doesn’t repair a weak system. It repeats it, loudly, across millions of answers, so a company that’s easy to find and plainly described gets that repeated, and a muddled one gets the muddle.

LLM Optimization, Defined Without the Hype

LLM optimization, sometimes shortened to LLMO, means making your company easy for large language models to find, understand and cite when someone asks a question you can answer. It covers the model’s training data, the search index it consults and the pages it fetches on request. It overlaps heavily with AEO and GEO, and our AEO vs GEO comparison sorts out which one comes first.

The acronyms keep multiplying, with LLMO, LLM SEO, GEO, AEO and AIO all in circulation, and I’d hate to see anyone pay for the same work five times under five labels. Google’s own guide says that, from its point of view, “optimizing for generative AI search is optimizing for the search experience, and thus still SEO.” It also tells site owners they can ignore tactics like “chunking” content and creating unnecessary AI text files such as llms.txt, as far as Google Search goes. When the plan is settled, our generative engine optimization services run it.

That’s useful, and I’d add one note to it. Google is one door. The others have their own crawlers, and they don’t answer to Google.

Two Clocks: What a Model Remembers and What It Looks Up

A language model is trained on a very large body of text up to a cutoff date. After that, it knows what it knows. Your new service line, your new office, your current pricing? Not in there until a future training run picks them up, if it ever does. That’s the first clock. Slow.

The second clock is retrieval. When an assistant searches, it pulls current pages from an index and writes the answer from them. Google’s May 2026 guide to generative AI features on Google Search calls the technique retrieval-augmented generation, and it adds query fan-out, where the model sends out several related searches to fill the gaps in the original question. One buyer prompt turns into a handful of lookups. Each lookup is a contest. Your page against theirs.

There’s a third path that gets forgotten. A user asks something and the assistant goes to fetch a specific page right then. OpenAI calls that agent ChatGPT-User, Anthropic calls its version Claude-User, and Perplexity calls its own Perplexity-User. OpenAI says that because a user triggered the request, robots.txt rules may not apply. Perplexity says its user fetcher generally ignores them.

Three Ways a Model Gets Your Page

Read across a row. Same company. Same website. Three bots, three sets of permissions, which means that one blunt “block AI bots” rule in a firewall can quietly shut off the single bot that was sending buyers your way.

What Each Assistant Documents, and What It Doesn’t

ChatGPT first. OpenAI’s crawler documentation treats OAI-SearchBot and GPTBot as independent settings, so a site can appear in search while keeping its pages out of training. Sites opted out of OAI-SearchBot “will not be shown in ChatGPT search answers, though can still appear as navigational links.” OpenAI also says a robots.txt change can take about 24 hours to register.

Claude splits the job three ways. Anthropic’s help article says disabling Claude-SearchBot “may reduce your site’s visibility and accuracy in user search results.” It also says that as of October 30, 2026 it honors a machine-readable “no AI training” preference signal, based on an IETF draft, and that a robots.txt rule naming ClaudeBot takes priority over the general setting.

Gemini is the odd one. Google’s common crawlers page says the Google-Extended token governs whether crawled content may train future Gemini models and ground answers in Gemini Apps, and that it does not affect inclusion in Google Search. AI Overviews and AI Mode run through the normal Search index. Google says it intends to support the same shared AI-training preference signal in early 2027.

Perplexity is the simplest of the four. Its docs say PerplexityBot surfaces and links sites in search results and isn’t used to crawl content for AI foundation models. At Perplexity you’re retrievable or you’re absent.

Now the part the vendor blogs skip. You’ll read confident claims that ChatGPT rewards agreement across sources, that Gemini rewards fresh first-party content, that Claude likes reasoned formats. I couldn’t find any of that in the crawler and Search documents I read for this post. None of the four publishes a list of ranking factors.

Maybe the claims are right. Maybe not. Test your own prompts before you rebuild a website around somebody else’s theory, because a theory that hasn’t been checked against your market is only a guess with a nice slide deck.

The Training Side: Decide, Don’t Chase

You can’t buy a place in a model’s memory, and none of the platform documents I read describes any way to request one, which leaves you controlling something much narrower. You choose whether training crawlers like GPTBot and ClaudeBot may collect your public pages, and that’s a legitimate business decision in either direction. OpenAI says plainly that disallowing GPTBot indicates your content shouldn’t be used in training. Block it if you want. I won’t argue the point.

Anyone who promises you a spot in model memory is selling a story, and the story has no documentation behind it.

What I’d do instead of chasing it is make the facts about your company identical everywhere a model might have read them. Your legal name. What you do. Who you serve. Where. Check your website, your LinkedIn page, your directory listings and any press mentions. If a model has read three conflicting descriptions of your firm, it can repeat any of them, and you don’t get to pick. Consistent facts help a future training run and today’s retrieval at the same time.

The Retrieval Side: Where the Work Pays Back

This is the job you can finish in a quarter. It has three parts, and the order matters.

• Access comes first. Pull a week of server logs and search them for OAI-SearchBot, Claude-SearchBot, PerplexityBot, Googlebot and Bingbot, then read the status codes, because a wall of 403 errors means a firewall or CDN setting is turning those bots away whatever your robots.txt says. Perplexity’s docs include a whole section on allowing its bots through a web application firewall. Nobody writes that section for a problem that never happens. Check anyway. Our post on AEO and GEO for SaaS walks through the same check for software companies.
• Pages come second. Fan-out means the model asks sub-questions about you. What do you do, who do you serve, where, what does it cost, how do you compare. Each needs a page that answers it in plain sentences. Microsoft’s October 2025 guidance says assistants break pages into smaller pieces, and that question-and-answer pairs can be lifted word for word.
• Proof comes last, and it’s the slow one. Other people’s words about you. Reviews, directory profiles, a vendor’s partner page, a podcast transcript. Google says chasing inauthentic mentions isn’t as helpful as it might seem, so earn them. Our look at how AI platforms decide which MSPs to recommend goes deeper on what counts, and our guide to brand mentions and AI search covers the off-site side.

Each assistant has its own quirks past that point. We’ve written those up for managed service providers in separate posts on how MSPs get cited by Perplexity, Google AI Overviews and AI Mode for MSPs and Bing and Copilot for MSPs. For the ChatGPT side on its own, see our guide to ChatGPT SEO for tech companies.

Measure It Like Pipeline, Not Vanity

Nobody can hand you a rank tracker for an assistant’s answer, because the answer can change from one run to the next, so you build a small system of your own and read the trend instead of the single result.

1. Write a panel of 15 to 20 prompts a real buyer would type. Write them once. Don’t edit them to flatter yourself.
2. Run the panel in ChatGPT, Claude, Gemini and Perplexity every month. Log who’s named, who’s cited and the date. One result is noise. Three months of the same direction is a signal.
3. Check the logs for each bot by name and status code, monthly.
4. Watch Search Console. Google’s guide points site owners to its Generative AI performance report for how content does in AI features on Search.
5. Last step, and a cheap one. Add “how did you hear about us” to your forms and sales calls, and count how many answers name an AI assistant.

None of that requires buying a tracking tool. Some are fine. A tool measuring a site nobody can crawl measures nothing at all.

What I’d Fix First at a Tech Company Under $50M

Say a 40-person managed services firm runs the four-assistant test. Made-up firm, so nobody takes credit for a result. Perplexity names two of its competitors and not the firm. The server logs show a run of 403 errors on PerplexityBot from a bot setting in the CDN. The main service page says “solutions tailored to your needs” and then stops.

Nothing mysterious there. It’s a system problem, and system problems get fixed in order, one step at a time.

1. Save the baseline panel before changing anything, so you can see what moved.
2. Fix access. It costs nothing, and OpenAI and Perplexity both say a robots.txt change can take about a day to register.
3. Rewrite the service pages to state who you serve, what you do, where, how engagements work and what they cost or how pricing is set.
4. Make the company facts match across your site, LinkedIn and every directory listing.

That’s the list. Ideas are cheap. Execution wins. And a buyer who asked an assistant for a provider and never heard your name is a deal you lost without knowing it existed.

C4 Solutions works with MSPs, telecom and technology companies up to $50M in revenue on the systems behind growth, and search is one of them, covered in our generative engine optimization services. If you want a second set of eyes on whether the assistants can find you, start with our AI visibility audit or book a growth strategy call.

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