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SEO and GEO

GEO, generative engine optimization, is making a company's pages easy for AI assistants to find and quote correctly, and it builds on classic SEO foundations.

The method at a glance

How the system works

What it does, in order, and what it never does.

The method, step by step Each step comes from the method described below.
  1. Structured data that matches the page
  2. Answers that stand alone
  3. Files for assistants
  4. Content by search intent
  5. Migrations that keep URLs

Connections

  • Structured data that matches the page → Answers that stand alone
  • Answers that stand alone → Files for assistants
  • Files for assistants → Content by search intent
  • Content by search intent → Migrations that keep URLs

What the system never does

  • It never fills pages with template text where only the product name changes.
  • It never promises a ranking. Rankings depend on parties KINTO does not control.
  • It never publishes a statistic it cannot source. Figures in pages come from cases or are labeled as the client's.
  • It never lets structured data contradict the visible page.

KINTO does both together, for companies that want to be found by search engines and cited by assistants such as ChatGPT, Gemini, Perplexity and Claude.

The problem

Most sites are written for a visitor who scrolls, not for a system that extracts an answer. The answer is buried, the structured data is missing or wrong, the same FAQ text is copied across pages and a migration quietly drops half the URLs. In a store migration, a heavy catalog without normalized data left product pages with little to cite. In a fintech case, several companies with offers that are hard to explain needed pages that answer different audiences’ questions.

How KINTO approaches it

  1. Structured data that matches the page. Product, service and FAQ markup comes from the same field as the visible text, so they cannot disagree.
  2. Answers that stand alone. FAQs are short, specific and written to be quoted without the rest of the page. In the fintech case KINTO segmented each audience by its main question and turned typical objections into frequently asked questions.
  3. Files for assistants. llms.txt and llms-full.txt, plus citation metadata.
  4. Content by search intent. In the study-abroad case, dozens of articles were written to answer the searches students make: requirements, costs, visas and scholarships by country.
  5. Migrations that keep URLs. In one migration every store URL was inventoried before the platform change and about 98% stayed on the same path.

What the system never does

  • It never fills pages with template text where only the product name changes.
  • It never promises a ranking. Rankings depend on parties KINTO does not control.
  • It never publishes a statistic it cannot source. Figures in pages come from cases or are labeled as the client’s.
  • It never lets structured data contradict the visible page.

Who it is for

Companies with a catalog, a service that needs explaining, or a platform change coming, that want to be quoted correctly. This page is the same method applied to kinto.co itself.

Case studies

Case studies that prove it

Fintech and crypto

Agentic websites

Fintech and crypto: websites where trust is built with clarity, not promises

Several fintech and crypto companies in Latin America and the US received bilingual sites from KINTO with segment landing pages, extensive FAQs and a layer that helps generative search engines cite them correctly.

Latin America, United States Read the case study
Industries

In depth

E-commerce and retail SEO & GEO · E-commerce and retail Read more
Insights

Related insights

Frequently asked questions
What is the difference between SEO and GEO?

SEO helps pages rank in search results. GEO, generative engine optimization, helps AI assistants find a page and quote it accurately. KINTO treats them as one job: correct structured data, FAQs that answer on their own, an llms.txt file and content organized by what the buyer is asking.

What exactly does KINTO add to a site for AI citations?

Structured data such as product, service and FAQ markup, llms.txt and llms-full.txt files, citation metadata, FAQs that make sense on their own and full English and Spanish parity with hreflang. In a fintech and crypto case, several companies got this layer on bilingual sites within about three weeks.

Can you do SEO for a store with thousands of products?

Yes. For an auto parts distributor with over 10,000 products online, KINTO delivered product SEO and batch alt text, and a specialty retailer's migration kept brand and type enrichment on thousands of products. The aim is useful product pages, not empty template text repeated at scale.

How many articles does a content plan need?

It depends on the search demand. In a study-abroad agency case, KINTO published dozens of search-intent articles, of a plan of more than a hundred, with hundreds of optimized images, in about six weeks. In a Bitcoin mining case, dozens of SEO articles accompany a multilingual catalog.

Can SEO results be guaranteed?

No. KINTO reports what it measures and says what it does not know. In the cases where KINTO built content or a site, later traffic, leads and rankings have no evidence yet, and the case pages say so. Page weight, URLs kept and structured data are measured; rankings are not promised.

What is GEO and does KINTO do it?

GEO, or generative engine optimization, is structuring a website so AI assistants can understand, trust and cite it. KINTO builds sites with llms.txt, structured data, hreflang sitemaps and answer-first writing, and applies the same approach to kinto.co. It cannot guarantee that any assistant will cite a site.

Have a problem like this?

Tell KINTO what is going on. The first conversation is to understand it, not to sell.

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