The Nexum Visibility Loop: How We Make a Business Visible to AI

Someone handed you a client's question: why does ChatGPT never mention us? It may have arrived as a forwarded blog post. You know the site and you know its rankings, and the question is still fair, because no report you run says what an AI answer contains. One owner in the threads linked below ranks second on Google for his own product and wrote that ChatGPT acts as if his business does not exist. Another watched ChatGPT list five competitors and skip his.

This post sets out the method Nexum-sol uses to answer that question, called the Nexum Visibility Loop, in enough detail to run the first cycle yourself. It also says which parts we keep.

What is the Nexum Visibility Loop?

The Nexum Visibility Loop is a five-stage cycle for getting a business named, accurately, in AI answers. The stages are Map, Anchor, Answer, Amplify and Measure, and the fifth feeds the first.

• Map. Find out where the business stands: which customer questions an AI engine answers with its name, which it answers with competitors' names, and whether the site can be read by a machine at all.
• Anchor. Give the engines one machine-readable identity: one business, one set of facts, stated in text a crawler can parse on the first pass.
• Answer. Rewrite the pages that matter so each one answers a customer's question in a block an engine can quote.
• Amplify. Get the same facts said in the places the engines read besides the site: reviews, directories, forums, local press.
• Measure. Ask the same questions again, count who is named, and hand the gaps back to Map.

It is a loop because the engines change, the competitors change, and one run of the work produces a number that is only useful against the next one. A checklist is finished once. The Loop is run monthly.

Is this SEO with a new name?

Partly, and the overlap is worth conceding at the start. Google's own guide to its generative AI features, last updated 10 July 2026, says that optimising for them is still SEO, and its AI features page says a page must be indexed and eligible for a snippet before it can appear in an AI Overview or in AI Mode. Most of Anchor, and all of the crawlability work in Map, is work a good developer already does.

Two things are different. The first is the engines. ChatGPT, Perplexity, Copilot and Gemini each read the web through their own crawlers with their own rules. OpenAI's documentation names OAI-SearchBot and says a site that blocks it will not appear in ChatGPT search results, and a ranking report never notices that. The second is the measurement. Search Console reports Google's surfaces and Bing Webmaster Tools reports Bing's, and as of 16 October 2026 OpenAI's page for site owners lists four crawlers and no report at all. Whether a business is named in a ChatGPT answer is measured by asking ChatGPT. The Loop exists to do that on a schedule, with the fixes in between.

Map: where does the business stand today?

Map produces one number and a short list of faults. The number is the AI Citation Rate: of a fixed set of questions a customer would type, asked across the engines on one day, the share whose answer names the business. The questions name the service and the place and never the business, because a question that contains the name measures nothing. Alongside the rate comes Share of Voice, the business's mentions divided by every business mention in the same answers.

The fault list is what a machine finds when it tries to read the site. Is the crawler allowed in, in the robots.txt file? Is the page text in the HTML source, or does it appear only after scripts run? Google's JavaScript documentation says that not all bots can run JavaScript, and that its own rendering of script-built pages waits in a queue. Do the website, the Google Business Profile and the directory listings agree on the name, the address, the phone number and the services?

When Map is done you have a dated number per engine, a list of which competitors were named instead, and the few faults that explain most of it. Owners who run this test for the first time are usually absent or described wrongly; the threads in the references show both. The number is uncomfortable, and that is what makes the next stage easy to approve.

Anchor: can a machine state who you are without guessing?

Anchor's outcome is an identity an engine can repeat with confidence: this business, this name, at this address, doing these things, reachable this way. The mechanisms come after the outcome, and they are mostly plain.

1. Let the crawlers in. OpenAI's documentation says a change to robots.txt takes about 24 hours to be reflected for ChatGPT search, and Perplexity's crawler page says up to 24 hours for its systems. When this is done the robots.txt file allows OAI-SearchBot, PerplexityBot and Googlebot, and you can read it in a browser.
2. Put the facts in the source. The name, the address, the phone number and a plain description of what the business does appear in the HTML of the home page and the contact page, without a script running. When this is done a search of the page source for the phone number finds it.
3. Say the same facts everywhere. The site, the Google Business Profile and every directory listing carry the same name, spelled the same way, with one phone number. When this is done there is no contradictory version of the business for an engine to pick.
4. Add the markup, after the above. Organization and LocalBusiness structured data, in JSON-LD, states the same facts in a form machines read directly, and the Schema.org validator confirms it parses. Be honest with the client about what this buys. Google's guide says structured data is not required for its generative AI features and that no new machine-readable files are needed to appear in Google Search. The markup still earns rich results in ordinary search, and it settles which business the name refers to when two share it. The llms.txt file belongs here with the same honesty: Google says it does not need one, and the file is for any engine that reads it. When this is done the validator shows the Organization block with no errors.

When Anchor is done, every machine that reaches the site finds the same facts in the text, in the markup and in the listings.

Answer: does one page answer the customer's question?

The Answer stage is where most of the lift comes from, and it rests on one principle: an engine quotes a block of text that answers a question on its own. We call it the answer block. Under a header written as the question a customer would type, the first paragraph answers it completely: who, what, where, for whom, how much, how to reach you. Specifics follow that a machine can repeat, such as turnaround times, price ranges and areas served. A short FAQ in the customer's own words closes the page. Someone who reads only the first paragraph has enough to call.

The evidence for writing this way is direct. In the paper presented at KDD 2024, Aggarwal and colleagues tested which changes to a page made generative engines more likely to draw on it, and they report that adding citations, quotations from sources and statistics raised a page's visibility in the answers by up to 40% in their benchmark. Concrete, attributable text gets used. Brochure copy does not.

Original data is the strongest form of this. A page that says something no other page says, a price list, a count, a result the business measured itself, gives an engine a reason to cite that page rather than a dozen that say the same thing. Google's guide makes the same point from its side when it asks for content that is valuable to the audience and is not a commodity.

When Answer is done, each priority page opens with a paragraph you could paste into ChatGPT and get a correct answer from.

Amplify: do other places say the same?

A site establishes what a business claims. Other sources establish whether to believe it. Amplify is the work of getting the business talked about, accurately, where the engines read: reviews on the Business Profile, the directories customers use, honest answers in the forums and groups where people ask about the trade, and coverage in the local tech and business press. Semrush's study of the domains AI engines cite most often found community sites and reference sites near the top of the list, and for a small business the practical version of that finding is the list above.

The guardrail is real consensus. Google's guide says that seeking inauthentic mentions across the web is not as helpful as it might seem, and the engines are not the only ones who notice. No bought reviews, no planted threads. Answer the question first and mention the business second. When Amplify is done, a search for the business by name turns up more than its own website, and what turns up agrees with it.

Measure: did the number move?

Measure re-runs Map's questions, unchanged, on the same day of the next month, across the same engines, and recalculates the citation rate and Share of Voice per engine. Change a question and you have measured the change in your questions. Keep the raw answers, because the wrong facts in them are next month's Anchor list and the competitors in them are next month's Amplify list.

The gaps go back to Map, and the Loop starts again. Absent everywhere points at crawlability. Named and wrong points at listings that disagree. Named on one engine and absent on the others points at what the other engines read. A steady citation rate with a falling Share of Voice points at Amplify.

We also keep one question that stands in for the whole method, and we call it the acid test: a question whose honest answer should name the business, asked every month, with a note of whether it does. For a tailor in Surulere it is the question a customer in Surulere would type. For Nexum-sol it is a question about which agencies in Lagos make businesses visible to AI engines. The month the business is named is the month the proof exists, and until then the number is a dated baseline.

What we publish, and what we keep

The five stages, the answer-block principle, the definition of AI Citation Rate and the acid test are published, in this post and the ones linked from it, because a method you cannot read is a method you cannot check. The audit checklist behind Map, the schema templates behind Anchor, the question library behind Measure and the scanner that runs it are the paid work, and they stay in the folder. A developer or an agency can run a full cycle from this page. What Nexum-sol sells is running it well, monthly, with the number on the table at the start.

The sequence we hold ourselves to is the one we ask of clients: run it on yourself first, prove the number can be moved, then sell it. The method above is the one Nexum-sol applies to its own site, and the first client any stage is tested on is us.

How to run the first cycle this month

1. Write ten questions a customer would type, naming the service and the place. Ask them in ChatGPT, Gemini, Perplexity and Copilot in fresh chats on one day and record who is named. You will see a number per engine and a list of competitors.
2. Check robots.txt and the page source for the crawler rules and for the phone number. You will see a blocked crawler, or a page whose text is not in the source, or neither, and each one is a one-line fix request.
3. Rewrite one page as an answer block for the question the business most wants to be named for. You will see a first paragraph that answers the question completely.
4. Fix the listings so they agree with the page, word for word where it counts, and ask two recent customers for a review. You will see the same facts in every place a search by name turns up.
5. Put the re-run in the calendar for the same day next month, with the same ten questions. You will see a second number, and the second number is the first one that means anything.

The customers asking are already there:

A business that has run one cycle knows whether it is one of the names those customers hear, and has a date on which it will know again.

One forward before you go: pick one client, write ten questions their customers would type, ask them in ChatGPT today in fresh chats, and write down who is named. That is Map, and it takes an hour.

Frequently asked questions

Is the Visibility Loop the same as SEO?

It overlaps with SEO and goes past it. Google's own guide says that optimising for its generative AI features is still SEO, and the crawlability and consistency work in Map and Anchor is work a good developer already does. The Loop adds the other engines, which have their own crawlers and rules, and the measurement, which is the AI Citation Rate across ChatGPT, Gemini, Perplexity and Copilot. No SEO dashboard reports that number. GEO and AEO are the trade names for the same ground; the Loop is the sequence we run on it.

How long does one cycle of the Loop take?

A month, because Measure re-runs the questions on the same day of the next month. The work inside the month is measured in days: the crawler and source fixes in a week, the priority pages rewritten in two or three, the listings corrected alongside. How quickly an engine picks up the change is not something anyone can promise, because OpenAI, Google and Perplexity publish no timeline for it. That is why the number is taken monthly rather than guessed at.

Do I need schema markup or an llms.txt file for this to work?

Not for Google. Its guide to generative AI features says structured data is not required for them and that no new machine-readable files are needed to appear in Google Search. Organization markup still earns rich results in ordinary search and settles which business a name refers to, so Anchor includes it after the plain fixes, never before them. The llms.txt file is for the engines that read it, and Google says it does not need one. Say this to the client in Google's words.

Can I run the Loop without hiring Nexum-sol?

Yes. The five stages, the answer-block principle, the definition of AI Citation Rate and the acid test are all on this site, and the steps at the end of this post are a full first cycle. What Nexum-sol sells is the audit checklist behind Map, the schema templates behind Anchor, the question library behind Measure, and the labour of running it monthly with the number reported. A developer with a spreadsheet and an afternoon a month can run the public version.

Which AI engines does the Loop cover?

ChatGPT, Google's AI Overviews and AI Mode, Gemini, Perplexity and Microsoft Copilot, measured separately. The crawler rules differ: OpenAI's documentation names OAI-SearchBot for ChatGPT search, Perplexity names PerplexityBot, and Google requires a page to be indexed and eligible for a snippet before it can appear in an AI feature. The question set is the same across engines so the numbers compare.

What is the acid test?

One question whose honest answer should name the business, asked in each engine every month, with a note of whether it does. It stands in for the whole method and it is the line a client can check without a spreadsheet. For a tailor in Surulere it is the question a customer in Surulere would type. For Nexum-sol it is a question about which agencies in Lagos make businesses visible to AI engines, and the month the answer names us is the month the proof exists.

What if the business has no website, only an Instagram page?

Then Anchor has nothing to anchor, because the crawlers that feed ChatGPT search and Perplexity read the open web and Instagram is not open to them. The business needs at least one public page that states its name, what it does, where, and how to reach it, plus a Google Business Profile that says the same. That page can be small. Without it, the Loop stops at Map with a number that cannot move.

References & sources

1. Google Search Central, Guide to optimizing for generative AI features on Google Search (last updated 10 July 2026; read 16 October 2026)
2. Google Search Central, AI features and your website (eligibility: indexed and eligible for a snippet)
3. Google Search Central, Understand the JavaScript SEO basics (last updated 4 March 2026: rendering queue, not all bots can run JavaScript)
4. Google Search Central, Organization (Organization) structured data
5. Google Search Central, Introduction to robots.txt
6. Schema.org, Schema Markup Validator
7. OpenAI, OpenAI bots documentation (OAI-SearchBot, OAI-AdsBot, GPTBot, ChatGPT-User) (read 16 October 2026: four crawlers, robots.txt changes take about 24 hours, no visibility report)
8. Perplexity, Perplexity crawlers (PerplexityBot, Perplexity-User) (read 16 October 2026: robots.txt changes may take up to 24 hours)
9. Microsoft Bing Webmaster blog, Introducing AI Performance in Bing Webmaster Tools Public Preview (10 February 2026)
10. Aggarwal et al., GEO: Generative Engine Optimization (KDD 2024)
11. Semrush, The most cited domains in AI search (November 2025)
12. Owner report, ranks 2nd on Google while absent from ChatGPT, r/SEO (November 2024)
13. Owner report, ChatGPT listed five competitors and skipped mine, r/IMadeThis (May 2025)
14. Pew Research Center, Americans and AI 2026 (5,119 US adults, February 2026)
15. BrightLocal, Local Consumer Review Survey 2026 (1,002 US adults)
16. DHL, E-Commerce Trends Report 2026 (29,000 shoppers across 29 markets)