Rethinking eCommerce: How I Used AI, LLMs and Vector Databases to Build a Smarter Shopify Experience

Over the last few weeks, I’ve been navigating a wave of change powered by AI.

The eCommerce space, especially around Shopify , is already crowded with apps for everything from reviews to analytics. But that raised a critical question for me:

What’s next?

Most Shopify store owners aren’t just looking for another app—they’re looking for intelligent, personalized solutions. They want faster answers, deeper insights, and simplified workflows. And that’s where Large Language Models (LLMs) and Generative AI come into play.

The Idea: Shopify as the Commerce Engine, AI as the Intelligence Layer

In my conversations with several clients especially DTC founders and heads of CRO, I noticed a common trend. They wanted to:

• Access store data in a conversational way
• Automate customer queries with personalized context
• Use AI to generate insights and even emails

So I began prototyping an AI-enhanced chatbot system that combines Shopify data with the power of semantic search using embeddings and vector databases.

Here’s what I did step by step.

Step 1: Extract Data from Shopify

Using Shopify’s Admin API, I pulled:

• Product Information (title, tags, descriptions, inventory)
• Orders (status, fulfillment, delivery, etc.)
• Customers (location, purchase history, segmentation tags)

This formed the core dataset for powering the AI layer.

// Example: Fetching products using Shopify Admin API (Node.js)
const shopify = new Shopify.Clients.Rest(SHOP, ACCESS_TOKEN);
const products = await shopify.get({ path: "products" });        

Step 2: Convert and Store Data in a Vector Database

Next, I needed a way to search semantically, not just by keywords. So I converted the textual data into embeddings using OpenAI’s Embedding API.

I experimented with a few vector databases:

• ChromaDB – Lightweight and easy to set up, but limited for scaling
• Pinecone – Great for production, very stable and fast
• Milvus – High-performance for large-scale projects, but a bit heavier to configure

Each of these stores high-dimensional vectors representing Shopify data—products, orders, even past chats.

Step 3: Use OpenAI Embeddings for Semantic Understanding

Once the data was vectorized, I used OpenAI's text-embedding-3-small model to enable semantic search across the product catalog or customer conversations.

For example, a query like “I’m looking for a red t-shirt with length 14” can now return accurate matches even if those exact words don't exist in the product title.

# Sample embedding code
response = openai.Embedding.create(
    input="Red t-shirt with size 14",
    model="text-embedding-3-small"
)        

Step 4: Connect Third-Party APIs

To go beyond Shopify, I also integrated other apps like Klaviyo, Gorgias, and custom CRMs by pulling their data via APIs and embedding that into the same vector space. This enabled a more holistic, cross-platform experience.

Step 5: Create a Unified /chat Endpoint

I built a central /chat API endpoint that:

1. Accepts natural language queries
2. Determines if the response needs to include sensitive data (like order status)
3. Either

Example:

POST /chat
{
  "query": "Can you tell me when my last order will arrive?"
}        

If authentication is verified, the response might be:

{
  "response": "Your last order #2025 was shipped yesterday and is expected to arrive on May 16."
}        

If not authenticated:

{
  "response": "Please log in to view your order details securely."
}        

Real-World Use Cases

Now with this setup, I could handle two very different scenarios:

1. Customer asks: “Where is my order?” → System checks auth, looks up order history, replies accordingly or asks for login
2. Customer says: “I want a red shirt under $30” → System queries vector DB, returns best matches from Shopify product catalog

What's Next: LLMs for CROs and CXOs

Beyond customer support, I’m seeing huge potential in using GenAI for:

• CXOs asking questions like “Which product had the highest margin in Q1?” and getting direct insights from their data
• CRO teams generating copy variations or pricing experiments using AI-powered suggestions

Imagine leadership teams chatting with their business data instead of digging into dashboards.

Final Thoughts

AI isn’t just a backend feature. It’s becoming the new interface.

This experiment proved how LLMs, vector search, and Shopify APIs can create truly intelligent commerce systems. As a developer or founder, if you're thinking beyond the app store and into smarter, more contextual eCommerce experiences, this is where the future is heading.

Let me know if you'd like to explore or collaborate on building AI-powered commerce solutions. I’m just getting started.