AI for Shopify, Magento and WooCommerce stores: search, recommendations, content and agent readiness
- Search and recommendations, measured
- Product content at scale, reviewed by people
- Order-status and returns answers from your policies
- Feeds and structured data ready for AI shopping agents
On-site: semantic search, recommendations and merchandising
"AI search" covers four different things: typo and synonym handling, matching by meaning, AI ranking and personalisation, and merchandising rules. We find out which of them your store is missing before choosing a tool.
The options range from the platform's own features (Shopify's semantic search, Adobe Commerce Live Search) through third-party engines to a search built on embeddings and a vector store when the catalogue or the language needs it. Our comparison of AI product search on Shopify, Magento and WooCommerce sets out the trade-offs.
Where AI helps a store
Product content at scale
Descriptions, attributes and translations drafted from your product data and checked by a person before they go live. Image clean-up and consistent attribute names come with it.Feed quality
Complete, consistent product feeds for Google Merchant Center and marketplaces: titles, identifiers, availability and prices that match the product page.Agent readiness
Product structured data that matches the page, accurate policies, and the platform settings that let AI shopping assistants find and recommend your products.Support assistant
An order-status and returns assistant that answers from your policies and looks up orders securely. See AI assistants.Agent readiness: getting found by AI shopping assistants
AI shopping assistants mostly do discovery today: they research and compare, and the purchase still usually happens on your site. Two open protocols define how stores take part in agent-led purchases, the Agentic Commerce Protocol (ACP) from OpenAI and Stripe and the Universal Commerce Protocol (UCP) led by Google, and the store platforms do most of the protocol work for you.
Your part is data quality, policy accuracy and measurement. Our guide to getting your store ready for AI shopping agents includes a 10-point checklist, and AI in eCommerce in 2026 covers the wider picture.
What we measure
Every change is measured against the numbers before it, using your analytics and the platform's reports.
- Search conversion: the share of searches that lead to a purchase, and revenue per search.
- Zero-result rate: searches that return nothing, the cheapest problem to fix.
- AI-referred orders: visits and orders arriving from ChatGPT, Perplexity, Gemini and other assistants.
- Assistant deflection: questions answered without a person, and the hand-offs that still need one.
Platform notes
- Shopify: semantic search applies automatically to online store search on qualifying plans and catalogue sizes; the Search & Discovery app adds synonyms, filters and recommendations. See our Shopify development work.
- Magento and Adobe Commerce: Live Search is included in the Adobe Commerce licence for supported versions; Magento Open Source stores use OpenSearch or a third-party engine. See Magento development.
- WooCommerce: search quality depends on the plugin or external engine you add; product data and the WooCommerce roadmap for AI agents are covered in our WooCommerce guide. See WooCommerce development.
What we have built
We have built a retrieval-grounded (RAG) assistant, an MCP server that lets AI applications use a business system through defined tools, embeddings-based semantic search, and an AI workflow agent whose actions a person approves. We describe them in general terms here, without client names or numbers; ask us for the details on a call.
The rest of our background is the systems AI has to plug into: we have built websites, online stores and mobile apps since 2005, today mostly on Laravel, Node.js, Magento, Shopify, WooCommerce and AWS, and in October 2026 we moved our own site from WordPress to Astro on AWS.
What we will not do
The same rules apply to every AI project we take on. They are the reason some projects end with a smaller scope, and a few with a recommendation to wait.
- Publish product claims or specifications that a person has not checked against the source data.
- Put an agent into production that acts without a person who can approve, stop and undo what it does.
- Train or fine-tune a model on your data, or send it to a provider that does, without your written consent.
- Publish AI-generated text on your site or send it to your customers without a person reviewing it first.
Frequently asked questions
Do I need a new platform to use AI features?
Usually not. Shopify, Adobe Commerce and WooCommerce all offer AI-assisted search or connect to engines that do, and most AI work on a store is about data quality, configuration and measurement on the platform you already have.
What is agentic commerce?
It is shopping where an AI assistant such as ChatGPT, Google AI Mode or Perplexity finds products, and sometimes completes the purchase, on a shopper's behalf. Open protocols such as the Agentic Commerce Protocol and the Universal Commerce Protocol define how stores take part; the merchant stays the merchant of record.
Can AI write our product descriptions?
It can draft them from your product data at scale, and that saves time. We always put a person between the draft and the store, because wrong specifications, invented claims and duplicate text cost you returns and search visibility.
How do you measure whether AI search works?
Before and after the change we compare the zero-result rate, the search click rate, the search-to-purchase rate and revenue per search, using your analytics and the platform's own search reports.
Which platforms do you work with?
Shopify, Magento and Adobe Commerce, WooCommerce and BigCommerce, plus custom storefronts.
Request a store AI readiness check
Send us your store URL and the platform you run. We will review search, product data and agent readiness and tell you where to start.
