GEO Expert in Bangladesh — Get Cited in AI Answers
Generative Engine Optimization for ChatGPT, Gemini, Perplexity and Google AI Overviews. Crawler access, entity signals and citable content — built on what the evidence actually supports.
What Is Generative Engine Optimization?
GEO is making your brand the one that AI systems name and cite when someone asks them a question. When a user asks ChatGPT, Gemini, Perplexity or Google's AI Overviews about your industry, the model assembles an answer from sources it can access and trusts. GEO is the work of making sure your site is one of those sources — and that your brand is described accurately when it appears.
The distinction from classic SEO matters: SEO gets you into a ranked list of links. GEO gets your brand named inside a generated answer, where there is no list — often just two or three cited sources. Being on page one of Google does not automatically mean being cited by an AI system, because they weight different signals.
What GEO Service Includes
- AI crawler access audit — verifying that GPTBot, ClaudeBot, PerplexityBot, OAI-SearchBot and Google-Extended can actually reach your content, and that your robots.txt distinguishes training crawlers from search-index and user-fetch crawlers
- Rendering risk check — confirming your content exists in the initial HTML rather than only after JavaScript runs, since a crawler that can't execute JS sees an empty shell
- Entity & schema consolidation — consistent Organization, Person and Service markup plus aligned descriptions across every platform, so AI systems resolve your brand as one coherent entity
- Citable content assets — original data, clear definitions and quotable statements that give an AI system a reason to cite you specifically
- Third-party footprint — presence on the platforms AI systems actually draw from most heavily for your industry
- llms.txt implementation — with an honest assessment of what it does and doesn't currently do, rather than overselling it
The Highest-Impact GEO Finding, Usually
In most GEO audits, the single biggest issue isn't content quality — it's access. Either the site blocks AI search crawlers in robots.txt without realizing the difference between a training crawler and a search-index crawler, or the content only renders after JavaScript executes, so anything that doesn't run JS sees essentially nothing.
Both are invisible from inside a browser, where everything looks fine. Both are quick to verify and usually quick to fix. That's why every GEO engagement starts with a technical access audit before any content work — there's no point optimizing content that AI systems structurally cannot read.
You can check this yourself with my free open-source AI-Ready Audit skill — it scores exactly these dimensions and requires no API key or paid tool.
Honest Expectations on GEO
GEO is a genuinely new discipline and parts of it are still unsettled. Some widely-promoted tactics have weaker evidence behind them than the marketing suggests — schema markup's measured effect on AI citation is real but modest, and llms.txt adoption by major AI systems remains largely unproven despite how often it's recommended.
What's well-supported: crawler access is decisive, content that can't render can't be cited, consistent entity signals help systems identify you correctly, and third-party presence on platforms AI systems draw from correlates strongly with citation. I focus effort there and tell you plainly which recommendations are proven versus which are reasonable bets.
Full reasoning in my GEO guide and evidence-based llms.txt breakdown.
Frequently Asked Questions
SEO ranks your website in Google's list of links. GEO gets your brand named and cited inside the answer an AI system generates. They overlap — both need crawlable, credible content — but GEO additionally depends on AI crawler access, consistent entity signals and being a source worth quoting, which classic ranking factors don't fully cover.
Primarily ChatGPT, Google AI Overviews and AI Mode, Perplexity, Gemini and Claude. Each crawls differently — OpenAI, Anthropic and Perplexity all run separate training, search-index and user-fetch crawlers, which is why a single blanket robots.txt rule often accidentally blocks the ones that actually matter for citation.
Check your robots.txt for the specific search-index and user-fetch crawler names, and verify your content exists in the raw HTML rather than only after JavaScript runs. My free open-source AI-Ready Audit skill checks both automatically, or I can run a full audit for you.
Honestly, the evidence is weak so far. Most published llms.txt files receive very few crawler requests, and no major AI lab has publicly committed to reading it in production. The clearest current consumers are developer AI tools reading documentation sites. I'd recommend it as a low-cost hedge for sites with real documentation, not as a general visibility lever — and I'd rather say that than sell it as more than it is.
Access fixes can show up quickly — if crawlers were blocked and now aren't, the change is structural and immediate. Citation frequency building takes longer, typically a few months, because it depends on content being crawled, indexed and then selected. Measurement is also genuinely less precise than classic rank tracking, which I'd rather be upfront about.
Ready to Get Started?
Free consultation, no commitment. Let's look at where you actually stand first.