AI-Powered Shopify Experiences

For recommendations and semantic search, the more product data and order history you have, the better the signal. I will tell you during discovery if your catalogue is large enough to see meaningful results — typically 200+ SKUs and 6+ months of order history gives good signal.

I select the model based on the task — GPT-4o for chat and content generation, Gemini Flash for speed-sensitive inference, and embedding models (OpenAI or Cohere) for semantic search. I am model-agnostic and use whatever delivers the best result for your use case.

I use Retrieval-Augmented Generation (RAG) — the AI is grounded in your actual product catalogue, FAQs, and policies rather than relying on general knowledge. This prevents hallucinations and ensures every response is relevant to your store.

Yes — I build escalation logic into the chat assistant so it recognises queries it cannot confidently answer and routes them to your support team via email, Gorgias, or whichever helpdesk you use.

No — AI inference happens server-side via API calls. The storefront loads normally and AI features render asynchronously, so they never block page load or affect your Core Web Vitals score.

Yes — I build an admin interface where you can define guardrails, approved responses, and topics the AI should not engage with. You have full editorial control over the AI's behaviour.