AI Search Isn't Magic: What Actually Works

AI-powered search is everywhere right now. Every search vendor claims their AI will transform your conversion rates. Some of it's genuine, some of it's marketing fluff dressed up as innovation. Let's talk about what actually works.

What "AI Search" Usually Means

When vendors say "AI search," they're typically talking about a few things:

Natural Language Processing

Understanding that "blue running shoes under £100" means the same as searching "shoes" and then filtering. Good NLP means customers can search how they naturally speak or type.

Semantic Understanding

Knowing that "trainers" and "sneakers" are basically the same thing. Or that someone searching for "gift for dad" probably wants something different than someone searching for "men's accessories."

Personalisation

Showing different results based on what we know about the customer—their browsing history, past purchases, location, and so on.

What Actually Improves Conversions

After implementing search improvements for dozens of stores, here's what consistently moves the needle:

Building Search That Works

We build custom search solutions that combine fast indexing, intelligent ranking, and natural language understanding. The key is tailoring the search behaviour to your specific catalogue and customer expectations.

The built-in Shopify search has improved, but for stores with large catalogues or complex filtering needs, a custom search implementation delivers significantly better results—and conversion rates.

What Doesn't Work (Yet)

Fully conversational search—where customers can ask complex questions and get accurate answers—isn't quite there for most implementations. The technology exists, but training it on your specific catalogue takes significant effort.

Visual search (uploading an image to find similar products) works for some categories like fashion and home décor, but needs high-quality product photography to be effective.

Our Recommendation

Start with the basics. Make sure your search handles typos, has good synonyms, and returns results quickly. Then look at your search analytics—what are people searching for and not finding? Fix those gaps before investing in fancier AI features.

Our Search Audit Checklist

Before any store we touch ships a new search experience, we run through the same list. It is unglamorous and catches most of the issues that genuinely hurt conversion:

• Zero-result queries — Pull the top 50. Any legitimate product query returning no results is a tagging, synonym, or catalogue gap we fix before anything else.
• Query analytics — Look at clicks-to-first-result. If the first result is regularly skipped, your ranking is wrong.
• Autocomplete quality — Suggestions should be products or categories, not random historical queries. Stale suggestions hurt more than they help.
• Mobile tap targets — Filter and sort on mobile is where most stores leak. Too small, too cramped, or badly labelled.
• Search analytics feedback loop — Without merchandiser access to query data, the search team is flying blind. This is often a tooling gap, not a search gap.

Buy vs Build: How We Actually Decide

For stores under about £3M revenue on Shopify, we usually start with a good managed vendor (Klevu, Searchspring, Algolia) and do the merchandising work ourselves. For stores with unusual catalogues — strong fitment requirements, deep variant systems, or B2B-specific visibility rules — we build custom. The line between the two is mostly about catalogue complexity, not revenue.

Buying search doesn't end the work. The biggest performance gains we've seen come from the six months after go-live: tuning synonyms, fixing zero-result queries, and aligning merchandising rules with what customers actually do. Without that, even the best search engine underperforms.

How This Relates to AI Shopping Assistants

If you're thinking about search, you also need to think about how ChatGPT, Perplexity, and Google AI Mode find your products. The underlying work is the same: clean catalogue, rich attributes, fast responses. We wrote a separate guide on optimising Shopify for AI shopping assistants — the two efforts share most of their infrastructure.