1. Executive Summary
A prospect asking ChatGPT, “Which Korean companies are best in this field?” is no longer a hypothetical scenario. The question is whether your company appears in the answer. Advertising spend is not what decides it. What matters is whether your website is designed so AI can read and understand it.
This article examines that design work—AEO (Answer Engine Optimization)—from the perspective of a corporate website. The previous article, “Beyond SEO: A GEO Strategy”, focused on the content question of what to publish. This time, the subject is the website infrastructure itself. We explain how AI identifies a company and how company information should be structured for accurate citation, moving from structured data to llms.txt, AI crawler policy, and an implementation plan.
In short, corporate website AEO comes down to five tasks: standardize the company description, add schema markup, structure FAQs, publish llms.txt, and monitor AI answers. The rest of this article explains why these steps matter, how to sequence them, and how to implement them.
Questions this article answers
What does a corporate website need in the age of AI search? How is AEO different from SEO? Should we create an llms.txt file? The answers are written for people at startups and small to mid-sized companies preparing to build or redesign a website.
By the end, you will have a requirements checklist that can be handed directly to a developer or agency.
2. Customers Now Ask AI About Companies
The starting point of the B2B buying journey is moving from the search box to the AI chat box. Buyers once searched keywords and opened ten vendor websites one by one. Now they describe their requirements to AI and receive a shortlist of three or four candidates. The website contest has shifted from competing for search rank to competing for a place on the shortlist.
2.1 Ten searches have become one question
Imagine a procurement manager looking for an industrial sensor supplier for a production line. Two or three years ago, that person might have searched for terms such as “industrial sensor manufacturer” and “proximity sensor supplier,” then visited each result to compare specifications and delivery records. Today, the question goes to Perplexity or ChatGPT: “Which Korean industrial proximity sensor manufacturers have supplied automotive parts companies?” Within seconds, AI returns three or four company names with a one-line summary of each.
In early 2024, Gartner forecast that traditional search-engine volume would fall 25% by 2026 because of generative AI chatbots. The direction matters more than whether the number proves exact. The first gateway to information is moving, and B2B purchases—where comparison and verification take longer—fit naturally with requests for AI to filter candidates against specific criteria.
2.2 Where does AI get company information?
The primary source is the official website. AI search products such as ChatGPT Search and Perplexity read the web while composing answers and attach source links. A company website is usually treated as the most authoritative document about that company. Press releases, news coverage, business directories, and company profiles on recruiting platforms serve as supporting sources.
Before placing a company in an answer, AI generally looks for the following:
- Identity: Can it understand in one sentence what the company does?
- Consistency: Does information on the website agree with external sources?
- Specificity: Do service and product pages identify customers, scope, and track record?
- Freshness: If the latest update is three years old, trust declines.
No website means no place on the shortlist
AI does not recommend a company it cannot verify. A business with no website—or one made only of images with no readable text—is a company with no usable data from the perspective of AI. A page can look impressive to people and still be effectively empty to a crawler.
In the search era, even a low-ranking result appeared somewhere. An AI answer often stops after naming three or four candidates, so exclusion can erase the company entirely from that question.
3. How Is AEO Different from SEO?
AEO targets the content of an AI answer, not a search ranking. SEO aims to put a page on the first page of search results. AEO aims to make AI mention your company early and describe it accurately.
3.1 SEO, GEO, and AEO: defining the terms
The three terms are often used interchangeably, and the practical work overlaps. SEO optimizes search rankings. GEO (Generative Engine Optimization) helps generative AI cite content. AEO focuses more specifically on inclusion in answers to questions. In this article, AEO means improving a corporate website so company information appears correctly in AI answers.
3.2 What changes for a corporate website?
The clearest difference is the unit of optimization. SEO works page by page and keyword by keyword. AEO addresses the company as a complete entity. AI is not simply ranking an individual page; it is building a model of what the company is.
| Category | SEO perspective | AEO perspective |
|---|---|---|
| Optimization unit | Individual pages and keywords | The company as a complete entity |
| Form of exposure | A link in a search-results list | Sentences and recommendations inside an AI answer |
| Company description | Minor variation between pages is usually acceptable | Must remain consistent across pages and external channels |
| Core work | Keywords, backlinks, and page speed | Structured data, FAQs, and consistent company information |
| Measuring results | Search Console rankings and traffic | Ask AI directly and monitor its answers |
| When it fails | The page falls a few positions | The company disappears from the answer and is treated as if it does not exist |
You cannot skip SEO and do only AEO. ChatGPT Search uses Bing indexes, while Perplexity relies on indexes built by its own crawlers. AI search operates on the traditional foundations of crawling and indexing. Without basics such as a sitemap, robots.txt, and metadata, AEO cannot even begin. If you are launching a new website, start with SEO Setup for the First 90 Days of a New Website.
4. Structured Data and Schema Markup
Structured data is a machine-readable business card embedded in a website. Visitors do not see it, but crawlers use it to identify the company name, industry, services, and contact information without guesswork. It lets AI verify your company instead of inferring what it might be.
4.1 What schema markup should you add?
A shared vocabulary from schema.org is inserted into the page in JSON-LD format. Google also recommends JSON-LD. Because it is a script block placed in the head without altering the visible HTML, it can be added to an existing website without changing the design.
The foundation is Organization schema. It includes the company name, primary URL, logo, address, and contact details, while the sameAs property connects social accounts and external profiles. sameAs is especially important because it links information scattered around the web and identifies it as belonging to the same company.
4.2 Which schemas should come first?
| Schema type | Where to use it | What it tells AI |
|---|---|---|
| Organization | Sitewide, usually anchored on the homepage | Company name, logo, address, contact details, and external profiles—the reference point for the company entity |
| Service | Service pages | What service is provided and who it serves |
| Product | Product detail pages | Product name, specifications, and price for product-level queries |
| FAQPage | FAQ pages | Question-and-answer pairs in a format AI can quote directly |
| LocalBusiness | Physical location pages | Location and opening hours for “near me” queries |
| BreadcrumbList | Every page | Site structure and relationships between pages |
| Article / BlogPosting | Articles and news pages | Publication date and author as signals of freshness |
Those seven cover nearly everything. For a corporate website, implementing Organization, Service, and FAQPage correctly completes roughly half of the essential work.
4.3 Why give FAQs special attention?
Because an AI answer is itself a question-and-answer format. Turn real sales and support questions—“How is the quote calculated?”, “How long will production take?”, and “Is maintenance included?”—into an FAQ page and connect it to FAQPage schema. This raises the chance that your own answer will be used when a prospect asks AI the same question.
Before schema: standardize how the company is named
If the website says “The BlueCanvas,” Naver Place says “BlueCanvas,” and a job posting uses a different legal name, AI may not be certain that all three refer to the same business. The same applies to addresses and phone numbers.
Standardizing the company name, address, and phone number (NAP) across every channel comes before schema markup. Even perfect markup cannot repair inconsistent source information.
5. llms.txt and AI Crawler Access
llms.txt is a site guide for AI. Place one Markdown file at the website root to explain what the company does and where its important pages are. It takes less than an hour to create and carries little downside.
5.1 What is llms.txt?
Jeremy Howard of Answer.AI proposed the convention in September 2024, and its specification is published at llmstxt.org. It starts from a simple problem: an LLM context window may be too small for an entire website, while HTML contains noise such as navigation and scripts. The proposal is to summarize the core of the site in one clean Markdown document.
The prescribed format is straightforward:
- Location: /llms.txt at the domain root, for example company.com/llms.txt
- H1 heading: The site or company name—the only required item in the specification
- Blockquote: One or two sentences summarizing the company
- H2 sections and link lists: Important pages in the format [Page title](URL): description
- Optional section: Secondary links that can be skipped when context is limited
Documentation-heavy sites sometimes add llms-full.txt with all content in one file. A corporate website rarely needs that much. A concise 20- to 30-line file with one company description, service pages, representative work, and a contact link is enough.
An honest view of llms.txt today
It is not an official standard. Neither OpenAI nor Google has confirmed that it uses llms.txt, and Google’s John Mueller has expressed skepticism by comparing it with the old keywords meta tag.
Even so, adoption is growing among developer-documentation sites, including Anthropic’s technical documentation. The cost is low and there is no meaningful downside. Publishing it now is more practical than waiting until its effect is conclusively proven.
5.2 Should you block or allow AI crawlers?
For most corporate websites, the answer is “allow them.” Publishers and paid-content businesses may have reasons to block training collection, but company and service information becomes more valuable when it is cited widely. Start by identifying which crawlers visit the site.
- GPTBot — OpenAI’s crawler for model training
- OAI-SearchBot — Indexing crawler for inclusion in ChatGPT search results
- ChatGPT-User — Real-time access when a user requests a page during a conversation
- ClaudeBot / Claude-User — Anthropic’s training crawler and real-time response access
- PerplexityBot — Perplexity’s search-index crawler
- Google-Extended — A robots.txt token controlling use for Gemini training; blocking it does not affect Google Search rankings
- CCBot — The nonprofit Common Crawl collector whose data is used to train multiple AI models
The key rule is simple: blocking search and real-time agents such as OAI-SearchBot, ChatGPT-User, and PerplexityBot removes the site from potential AI-search citations. Allow them if you want to appear in AI answers. Training crawlers such as GPTBot, ClaudeBot, and CCBot are a policy choice, but blocking them offers little benefit when the content itself is not the product. robots.txt can set rules for each user agent, so begin by checking how your current file treats them. More sites than expected have forgotten a blanket Disallow: / rule.
6. AEO Action Plan for a Corporate Website
The plan has five steps. Sequence matters because the output of each step becomes input for the next.
Step 1 — Standardize the company description
First decide how to describe the company in one sentence. It should be factual and include the industry, target customer, and differentiator. Phrases such as “a global innovation company that puts customer value first” tell neither people nor AI anything useful. “Twelve years implementing MES for mid-sized manufacturers across 80 production lines” is much more likely to be cited. Use the final sentence consistently on the homepage, About page, footer, Naver Place, recruiting posts, and press-release boilerplate. AI trusts descriptions repeated consistently across sources.
Step 2 — Implement schema markup
Put the sentence and company data from Step 1 into Organization schema, then add Service schema to each service page. If an external developer handles the work, specify: “Implement it in JSON-LD and submit a screenshot showing that it passes Google’s Rich Results Test.” Include the acceptance criterion so the task does not end with superficial markup.
Step 3 — Structure the FAQ
Select ten questions actually received in sales meetings and support channels. Answer each in two to four sentences, with the conclusion first. Publish them as a page and connect FAQPage schema. Real questions are better than invented ones because they closely match what prospects ask AI.
Step 4 — Publish llms.txt
Use the format from Section 5 and upload the file to the domain root. Update it whenever the service lineup changes and review it at least once per quarter. The company description from Step 1 is reused here.
Step 5 — Monitor AI answers
Once a month, ask ChatGPT, Claude, Perplexity, and Gemini the same set of questions and record their answers.
- “Recommend strong Korean companies in this field.” — Does your company make the shortlist?
- “What kind of company is [company name]?” — Are any statements inaccurate?
- “Compare [company name] with [competitor].” — What evidence does the comparison use?
Incorrect answers usually arise because the relevant information is absent or ambiguous on the website. You cannot directly correct the AI, so improve the source material. In GA4, checking referral traffic from chatgpt.com and perplexity.ai also reveals how visits from AI are changing.
Three free validation tools
Use Google’s Rich Results Test at search.google.com/test/rich-results to confirm schema recognition, the Schema.org Validator at validator.schema.org to find syntax errors, and Search Console to check indexing. All three are free.
If you are commissioning a redesign, ask during the quotation stage whether structured data and llms.txt are included. An agency unfamiliar with those items is unlikely to deliver serious AI-search readiness.
7. Conclusion and Checklist
Your website has gained another reader: people, search engines, and now AI. The third reader does not see the design. It reads only text and data, and it can repeat only what it has read. Everything in this article prepares the site for that reader.
Final checklist
- Standardized company description: Is the same one-sentence description used across the website and external channels?
- Schema markup: Are Organization and Service schemas implemented in JSON-LD and passing the Rich Results Test?
- Structured FAQ: Is there an FAQ based on real customer questions and connected to FAQPage schema?
- llms.txt: Is it published at the domain root and updated to reflect the current service lineup?
- Crawler policy: Does robots.txt allow AI-search crawlers such as OAI-SearchBot?
- Monitoring routine: Do you ask major AI services about the company and record the answers once a month?
It is not unusual if none of the six boxes can be checked yet; most corporate websites in Korea are in the same position. That also means companies that prepare first can occupy their category’s AI answers for some time. Start before your competitors read this article.