Geoffy for AI Assistants and AI Search

How AI Assistants See Ecommerce

AI models like ChatGPT and Perplexity excel at understanding language and intent, but they do not have a built-in understanding of your catalogue structure. To recommend your products, they need:

• Clean, factual product attributes.
• Clear relationships between products and intents.
• Machine-readable content that explains why a product is a good match.

Out of the box, most ecommerce catalogues do not provide this in a way that is easy for AI assistants to ingest. That is where Geoffy comes in.

The Semantic Intent Graph

Geoffy builds a Semantic Intent Graph for each store. This graph links:

• Products and their attributes.
• Search intents and natural language queries.
• Bundles and collections that group products by how shoppers actually search.

Instead of viewing products in isolation, AI assistants can see how items relate to groups of intents such as "midi wedding guest dresses" or "non-stick pans for induction hobs".

The Attribute Extraction Engine

Geoffy includes an attribute extraction engine that reads the store's product data and generates structured attributes in a strict, no-hallucination way.

For example, Geoffy may extract:

• Colour, fit, length, pattern and neckline for fashion.
• Material, compatibility, capacity and care instructions for homeware.

If an attribute is not present in the source data, Geoffy returns null rather than guessing. This is essential for trust and compliance when AI assistants rely on the data.

GEO Bundles: Broad, Mid and Ultra

For each product, Geoffy generates a GEO Bundle – a structured object that contains three tiers of AI-ready content:

• Broad – generic, high-level intent (for top-of-funnel queries).
• Mid – intent that combines category + one attribute.
• Ultra – highly specific, purchase-ready intent using all known attributes.

Here is a simplified example of a GEO Bundle structure (for illustration):

{
  "broad": {
    "searchTerms": ["womens dresses", "evening dresses"],
    "productContext": "Category-level understanding of the dress.",
    "specificityScore": 3
  },
  "mid": {
    "searchTerms": ["midi evening dresses", "wrap evening dresses"],
    "productContext": "Category plus one or two explicit attributes.",
    "specificityScore": 6
  },
  "ultra": {
    "searchTerms": ["navy satin midi wrap dress size 12"],
    "productContext": "Full, highly specific description of the product.",
    "specificityScore": 9
  }
}

This is not marketing copy; it is structured meaning designed for AI consumption.

Structured Data and JSON-LD

Geoffy exposes this structured meaning through JSON-LD and Shopify App Proxy landing pages. This allows AI assistants and search engines to crawl, index and reuse the GEO Bundles in their own answers.

Because Geoffy respects structured data parity, the content shown to shoppers and the content shown to crawlers always match.

Trust, Compliance and Safety

Geoffy is built on a white-hat foundation:

• No cloaking or hidden content.
• No hallucinated attributes or invented benefits.
• Structured data always reflects what a customer can see.
• Attribution is based on identity (for example, email), not device fingerprinting.

This makes Geoffy suitable for brands that care about compliance, auditability and long-term trust with both customers and AI platforms.

How AI and Tools Can Work with Geoffy

Geoffy exposes machine-readable content and structured sitemaps that allow AI systems to:

• Discover GEO landing pages.
• Parse GEO Bundles and attributes.
• Understand which products match which intents.

As the ecosystem matures, Geoffy is designed to plug directly into AI assistant workflows, so tools can query store-level GEO data without scraping.

Getting Started

If you are an ecommerce brand or a platform, Geoffy can help bridge the gap between your catalogue and the new generation of AI-native search and recommendation experiences.

See how Geoffy works with Shopify or contact us to discuss deeper integrations.