How to Improve Brand Visibility in ChatGPT: A Practical GEO Workflow

People searching for “how to rank in ChatGPT” usually want the same outcome: when a buyer asks an AI system about a product category, provider or solution, will the brand enter the answer? ChatGPT responses can vary by prompt wording, model, market, language, time, account context and web-search availability. There is no single, permanent brand ranking shared by every user. GEO improves the conditions for a brand to be discovered, understood accurately, cited and considered, then measures those signals through repeatable tests.

Start with the website. Priority service, case and insight pages should return successful status codes, work on mobile, remain reachable through internal links and use consistent canonical and language annotations. Review page speed, heading structure, stable URLs and the sitemap. Structured data can help search systems understand a page, but it must match visible content and cannot guarantee inclusion in an AI answer.

Check whether robots.txt, the CDN, firewall or security software is blocking access that the business intends to allow, including Googlebot, Bingbot and OAI-SearchBot. Server logs are more informative than robots.txt alone. Review status codes, crawl frequency, requested pages and repeated 403, 429 or 5xx responses. Decide access according to content rights and privacy requirements; never expose private systems simply to pursue AI visibility.

Keywords still matter, but AI conversations often contain context, constraints and comparison criteria. Build the prompt set from sales records, support questions, on-site search and customer interviews. Include brand, category, use-case, comparison and risk questions. Begin with 20 to 30 commercially relevant prompts, keeping most stable for comparison. Research Chinese and English demand separately rather than treating literal translation as localisation.

• Brand questions: who the company is and what it provides
• Category questions: which providers or approaches are available
• Use-case questions: what a specific type of company should do
• Comparison questions: how options differ and who each suits
• Risk questions: what commonly goes wrong during selection or delivery

Test the same prompts across target platforms such as ChatGPT, DeepSeek and Doubao. Save the exact prompt, full answer, source links, date, language, market, login state and web-search setting. Record whether the brand appears, whether the description is accurate, which pages are cited and why competitors enter the answer. Position alone is not a conventional search ranking; consideration, accuracy and supporting sources are more useful signals.

Translate findings into page decisions. A provider-comparison prompt may require a service-selection page or case rather than another definition. Missing delivery information calls for procurement content. Incorrect brand facts should be corrected across the website and authoritative profiles first.

Prioritise pages that help a reader make a decision: practical guides, selection criteria, fair comparisons, use-case solutions, documented cases, FAQs and glossaries. Each page should solve one defined problem and explain conditions, steps, evidence, limitations and the next action. Connect new content naturally to relevant services, cases and topic hubs.

The website explains how the company describes itself. Independent sources help buyers verify that description. Keep the legal entity, team, service scope, cases, contact details and author information accurate, while earning relevant reporting, directory entries, partner references, expert citations and genuine reviews. Link volume is not the only goal; relevance, independence and verifiability matter more.

Retest priority prompts every two to four weeks. Track brand presence, shortlist inclusion, factual accuracy, valid citations and competitor changes, always preserving sample size. Every finding should trigger a practical response, while website visits, branded searches, qualified enquiries and sales quality are reviewed alongside AI visibility.

• Select the priority service and market
• Build 20 to 30 questions drawn from real buyers
• Review key pages, crawler permissions and server logs
• Complete a baseline test across target AI platforms
• Map each gap to a service page, case or guide
• Improve accurate entity information and verifiable evidence
• Retest under comparable conditions and review enquiry data

In a GEO engagement, WEPR reviews technical access, buyer questions, page content, source evidence and monitoring data separately before setting priorities. This cannot promise an uncontrollable “number one ranking in ChatGPT.” It gives the team a reason for the brand's absence, a defined next action and evidence that any improvement survives a repeat test.