Correctly described and recommended inside ChatGPT: two different jobs

Being described correctly inside ChatGPT and being recommended by it are two separate outcomes with two separate causes. An accurate description depends on whether consistent, machine-readable facts about your business exist in public: legal name, services, location, who you serve. Being named in a recommendation depends on something else: whether independent third-party sources mention you in the context of the question being asked, and whether ChatGPT's search crawler is permitted to read your pages when the answer is assembled. Most Malaysian SMEs try to fix the second before the first. That order rarely works.

This piece is written for owners and practice managers of Kuala Lumpur professional services firms, the 2 to 50 staff clinics, law and accounting practices, property agencies and specialist traders with no in-house marketing team. It is a diagnostic: how to tell which of the two problems you actually have before you spend money on either. Disclosure: it comes from Geo One, a Generative Engine Optimisation (GEO) and Answer Engine Optimisation (AEO) agency in Kuala Lumpur.

What is the difference between being described and being recommended?

A description is what an assistant says when someone types your business name. A recommendation is what it says when someone types a need, such as corporate lawyer in Kuala Lumpur for a shareholder dispute, with no brand name in the query.

Those answers are built differently. The description leans on whatever the model already holds about your business as an entity, topped up by whatever it retrieves live. The recommendation is mostly a retrieval and shortlisting job, drawn from pages the assistant can fetch and from sources that already group businesses like yours together.

That distinction matters for budget and effort. Correcting a description is mainly a data-consistency problem you control: your own site, your own structured data, your own listings. Being eligible for a recommendation depends on sources you do not own and cannot command. Nobody can promise you a place in an AI answer, and you should be sceptical of anyone who does. What can be improved is the quality and consistency of the signals those systems read.

Can ChatGPT's search crawler actually read your website?

This is the first thing to check, because everything else sits behind it. OpenAI operates more than one crawler, and they do different jobs. According to OpenAI's platform documentation, GPTBot is associated with model training, while OAI-SearchBot is connected to ChatGPT's live search features, the path that surfaces websites inside answers. Blocking them is a publisher's right. Blocking the search agent by accident, while intending only to opt out of training, is a different matter.

That mix-up is common. Many Malaysian SME sites sit behind Cloudflare, where AI crawler controls are managed at the edge rather than in robots.txt, and defaults have changed over time. Cloudflare's developer documentation sets out how its bot and AI crawler controls behave. If your site was built or migrated recently and nobody has opened that panel, you do not know your current setting. The same trap applies to Claude: allowing one Anthropic crawler but not the search-time retrieval crawler leaves the live-web path unable to read you.

The fix takes minutes. The diagnosis is the part people skip. Geo One's free AI visibility scanner checks crawler access as part of the first pass.

How do you check what ChatGPT says about you today?

Ask it deliberately, and write the answers down. Run three prompts, three times each, in the languages your buyers actually use.

Start with your exact registered business name. Then your name plus your city. Then the buying question, phrased the way a client would phrase it, with no brand name in it. Repeat in Bahasa Malaysia and, if relevant, Mandarin. Answers vary between sessions, so a single run tells you very little; a pattern across several runs tells you a lot. We wrote separately about tracking prompts in Bahasa Malaysia, because the sources an assistant reaches for in Malay are often not the same ones it reaches for in English.

Record three things each time: whether you were named at all, whether the facts were right, and which websites were cited. That last column is the useful one, it shows which third-party pages are feeding the answer in your category.

Reading the result

• Name query returns wrong or outdated facts. A data-consistency problem. Your details disagree across your site, your Google Business Profile and whatever directories carry you, and the assistant is picking the loudest version. Our guide on what to do when an AI assistant gets your business wrong covers the correction sequence.
• Name query returns nothing at all, or a confident description of a different company. Check crawler access first. If the live-web path cannot fetch you, no amount of on-site tidying will show up in an answer.
• Name query is fine, need-based query never mentions you. Nothing is broken. You are simply not present in the third-party sources the assistant pulls from when it builds a shortlist. That is a different budget line, and a slower one.

Re-test the same prompts weeks apart rather than days apart. Facts the model holds and facts it retrieves live update on different clocks, so a corrected listing can start appearing in a retrieved answer well before it changes what the model says unprompted.

What makes a business eligible to be named in a recommendation?

Mentions you did not write yourself.

Consider what the assistant has to do when the query carries no brand name: produce a shortlist. To do that it needs a document that already groups or compares businesses in your category and city. Your own service page is not that document. It argues for exactly one firm, and an assistant assembling a list of five has no reason to read it as a list of five. The pages that do the job are review aggregators, professional directories, editorial roundups, association and chamber member listings, and local media, organised by category and location, and written by someone other than you.

This is also why the gap falls hardest on small firms. A national brand is mentioned in enough independent writing to be retrievable for almost any related question. A six-person practice in Bangsar may exist on the public web only in its own words, and a retrieval system cannot surface a comparison that nobody has written. The remedy is slow and unglamorous, earning those mentions one source at a time, which is why it belongs in a separate plan from the week of tidying that fixes a description.

Both are worth doing. Neither substitutes for the other, and running them as one line item is how SMEs end up paying for the wrong half.

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Geo One is a GEO and AEO agency based in Kuala Lumpur, serving Malaysian SMEs with 2 to 50 staff who want their businesses found and accurately represented inside AI assistants.