What Is GEO (Generative Engine Optimization)?

GEO (Generative Engine Optimization) is the practice of making your business visible inside AI-generated answers — on tools like ChatGPT, Gemini, Perplexity and Google's AI Overviews — so you get surfaced and cited when people ask AI for recommendations.

At INH System, we help businesses build the structured content and entity signals required to appear in those answers.

Here's a scenario that's already happening. A potential client opens ChatGPT and types: 'Which software company in Malaysia should I hire to build a custom web app?' ChatGPT responds with three recommendations. One of your competitors is on that list. You are not — even though you've been doing this for years and your work is genuinely better. That's the GEO problem.

Traditional SEO gets you onto a results page. The user still has to choose you from a list. GEO — Generative Engine Optimization — is different. When AI generates an answer, it doesn't show a list. It picks sources it trusts and writes a response. If you're not in that trusted pool, you simply don't exist in that answer. No link, no mention, nothing.

The question GEO tries to answer is: why does an AI engine trust and cite one business over another? And the answer isn't mysterious — it's about entity clarity, consistent information, and content that is structured to be extractable.

Google's AI Overviews — and to a lesser extent ChatGPT and Perplexity — don't work the way a traditional search ranking does. They're not picking the highest-authority page and summarising it. They're doing something closer to passage extraction: scanning content at the sentence and paragraph level, finding pieces that directly and confidently answer the question being asked, and assembling a response from those extracts. The page that wins isn't necessarily the one that ranks highest — it's the one whose content is most cleanly extractable. A paragraph that opens with a direct statement, uses plain language, and doesn't require surrounding context to make sense is far more likely to get pulled in than one that hedges, qualifies, or assumes the reader already knows the background. This is a fundamentally different optimisation target from traditional SEO, and it explains why strong-ranking pages can still be invisible in AI-generated answers.

This also means the opportunity isn't exclusively held by the largest and most established brands. A smaller business with well-structured, specific, directly answerable content will outperform a large competitor whose site is full of polished but vague marketing copy. AI engines don't respond to brand prestige the way human readers might — they respond to clarity and specificity. That's an advantage worth understanding and building on, particularly if you're competing in a market where the bigger players have invested heavily in SEO rankings but haven't touched their content structure.

Most of the work in GEO falls into three areas. First, entity definition — making sure AI systems have a consistent, accurate picture of who you are, what you do, and where you operate. If your business name, services and location are described differently across your website, your Google Business Profile and third-party directories, AI models get a confused picture and tend to leave you out.

Entity definition in practice means more than having a website. It means ensuring that every major surface your business appears on — your Google Business Profile, your LinkedIn company page, your website's About page, industry directories, and any media coverage — describes you in consistent terms. Same name format, same service descriptions, same geographic focus. If your website describes you as a 'software development company' and your Google Business Profile categorises you as a 'web designer' and your LinkedIn says you're a 'technology consultancy,' an AI model trying to understand who you are gets a fragmented picture and may decide you're not a clear authority on anything. Practically, entity work involves: adding an llms.txt file to your site root (a plain-text file AI crawlers read, describing your business, services and intended audience in clear terms), standardising your NAP — name, address, phone number — across every directory and profile, implementing Organisation schema markup on your homepage, and auditing third-party profiles for accuracy.

Second, citation-ready content. AI models extract answers from content that is direct, factual and clearly structured. Vague, promotional copy is hard to cite. Specific, well-organised answers are much easier for a model to pull from and attribute accurately.

The difference between citation-ready and non-citation-ready content is easier to see than to describe. Consider two ways of answering 'how long does a custom software project take in Malaysia?' Version A: 'Project timelines can vary significantly depending on a wide range of factors, including complexity of requirements, team size and chosen technology stack.' Version B: 'A custom software project in Malaysia typically takes three to nine months. Simple web applications take three to four months. Platforms with complex integrations, data logic or multi-user systems take six to nine months or more.' Version A is advice. Version B is an answer. AI models prefer Version B because it can be extracted and quoted directly, without requiring surrounding context to make sense. Version A reads like every other piece of agency content on the internet. Version B is specific enough to cite.

Third, authority signals. AI systems are trained on human-generated data, and that training skews toward sources that humans already trusted — publications, directories, review platforms, industry bodies. Building genuine presence on those surfaces moves you into the pool AI draws from.

For a Malaysian B2B company, the authority signals that matter most for GEO are: being mentioned in credible industry publications (The Edge, Star Tech, relevant trade media), having a presence on aggregators like Clutch or GoodFirms, genuine client reviews on Google and industry platforms, and case studies or results data that third-party sources have referenced. What matters is that these mentions exist in the web corpus AI models train on and crawl — not just on your own site. Your own claims about your expertise carry far less weight than someone else mentioning you as a credible operator in your space.

Right now, most businesses in Southeast Asia are not thinking about GEO at all. Their competitors are not thinking about it either. That window will close — the same way the early window on SEO closed for businesses that ignored it until 2015 and then spent years catching up. The difference is that GEO compounds faster, because AI models update and learn, and the entities they already know well have a natural advantage.

Strong SEO foundations help GEO — clear structure, accurate information, and genuine authority carry over. But SEO alone does not cover entity definition, cross-platform consistency, or the specific content formatting that makes you extractable. The businesses that treat GEO as just 'more SEO' are the ones that will be surprised when a competitor with weaker rankings keeps appearing in AI answers ahead of them.

GEO results look different from SEO results — and measuring them requires a different approach. You won't see a citation in ChatGPT appear in your Google Analytics traffic data. The practical measurement is: manually query AI tools (ChatGPT, Gemini, Perplexity) with the questions your buyers would ask, and note whether your business appears. Do this monthly and track changes over time. Supplement that with brand mention monitoring using tools like Brand24 or Google Alerts, looking specifically for references in AI-generated content and AI-affiliated platforms. The most concrete signal of GEO working is when inbound enquiries start referencing AI tools as the source — 'ChatGPT recommended you,' 'I found you through Perplexity.' That signal is more valuable than any dashboard metric because it represents a buyer who was given your name by an AI they trust.

The practical starting point is an entity audit: do AI tools currently know who you are? Open ChatGPT and Perplexity and query them with your business name, your service category, and the specific questions your buyers ask. If you're absent, or the information returned is incomplete or wrong, that's your first problem. Fix the foundational signals before anything else — consistent NAP, Organisation schema, an accurate and complete Google Business Profile, a clean llms.txt — because content without entity clarity is content without an author AI can trust. Once those foundations are in place, focus on restructuring your existing content for extractability before publishing anything new. A well-structured answer to a question your buyers already ask is worth more for GEO than ten new articles that hedge and qualify rather than answer directly.

The businesses we've seen gain the most traction from GEO in the early stages are not the ones with the largest content libraries — they're the ones that answered three or four specific questions with unusual clarity and built consistent entity signals around a tightly defined service area. GEO rewards precision. If you try to be visible for everything at once, you'll likely be invisible everywhere. Pick the two or three questions your best-fit buyers ask most, build the clearest answers you can for each, and make sure every major platform that describes your business tells the same story. That's a realistic starting point — and it's a better use of early budget than any content sprint.