How can your website prepare for AI-Powered Search: case study kayamoko.com

As artificial intelligence reshapes the digital landscape, businesses must adapt to remain visible and competitive. AI-powered search tools like Google’s Search Generative Experience (SGE) and advanced answer engines are changing how consumers discover products and content online. For Kayamoko, an artisan gemstone jewelry brand, adopting strategies for AI-driven search is not just an option—it’s essential for future growth.

Here’s a step-by-step guide on how Kayamoko is implementing in this evolving search environment and in the hope that this can be useful to your business too.

Step 1: Optimize Product Descriptions for AEO

• Rewrite product descriptions to answer user questions directly.
• Example for Kayamoko’s “Tiger Eye Bracelet”:

“What are the benefits of wearing a Tiger Eye bracelet? Tiger Eye is believed to enhance confidence, courage, and focus. This bracelet is ideal for people seeking emotional balance and clarity.”

This makes it more likely to be featured in AI-generated product summaries.

Step 2: Add Structured Data (Schema Markup)

• Use Product Schema to help AI identify key product details:
  • Product Name
  • Description
  • Price
  • Availability
  • Reviews
• Example: Add JSON-LD structured data for each bracelet:

{
  "@context": "https://schema.org/",
  "@type": "Product",
  "name": "Tiger Eye Gemstone Bracelet",
  "description": "A handmade tiger eye bracelet for confidence and focus.",
  "sku": "TYB001",
  "offers": {
    "@type": "Offer",
    "price": "3500.00",
    "priceCurrency": "KES",
    "availability": "https://schema.org/InStock"
  }
}

This helps Google AI understand and surface Kayamoko’s products in AI search snapshots.

Step 3: Create AI-Friendly Blog Content (GEO Strategy)

Start publishing AI-prompted blog posts answering common gemstone questions:

• “How does Blue Lace Agate benefit your energy?”
• “The History of Gemstone Jewelry in African Cultures”
• “How to Clean and Recharge Healing Bracelets”

These evergreen topics position Kayamoko as a trusted source for AI models like Google SGE or ChatGPT to cite.

Step 4: Use llms.txt (Emerging Practice for Generative Engines)

Although still early-stage, some sites are adopting llms.txt files to declare usage permissions for AI models.

• Example entry for Kayamoko’s llms.txt:

User-agent: *
Allow: /

This allows LLMs to index their content, making it available for AI answer engines.

Step 5: Monitor AI Search Performance

Use tools like:

• Google Search Console (SGE Reports)
• Semrush (AI Features & Featured Snippets)
• Ahrefs (SERP Features Monitoring)

Kayamoko can track whether its products or blogs appear in AI-generated search results.

Example Scenario:

Search Query in 2025:

“What is the meaning of Tiger Eye gemstone bracelet?”

SGE Result Powered by Kayamoko:

“According to Kayamoko.com, Tiger Eye bracelets enhance courage, clarity, and emotional stability, making them a popular choice for everyday wear.”

Kayamoko earns brand visibility even if users don’t click through (zero-click result) while also increasing trust and potential traffic.

Benefits for Kayamoko:

• Increased brand authority in AI-powered search.
• Higher organic visibility, even with fewer clicks.
• Better qualified traffic from users with intent to buy gemstone jewelry.

Based on this case study, it is obvious that Kayamoko is on track to and AI powered search based on this example. KYTCH codelab has enabled www.kayamoko.com be able to achieve this by adding custom JSON_LD code to its product descriptions to further define them for search.