Retail technology that helps shoppers find it and buy it.
Shoppers now expect to describe what they want in plain language and get relevant results, while your team juggles inventory, orders and marketplaces across too many systems. We help online and omnichannel retailers improve search, personalisation and operations with AI where it pays, and build the integrations underneath with an AI-native team that ships quickly and tests properly.
Most online stores do not have a traffic problem. They have a finding problem.
Look at your site search logs and you will usually find the same story: queries that return nothing because the shopper used a different word than your catalogue, filters that do not match how people think about products, and top results ranked by whatever was added most recently. Visitors who search are often the ones closest to buying, and they are the ones being let down.
AI has raised expectations here. People used to conversational assistants now type "waterproof jacket for hiking in humid weather" and expect the store to understand. Semantic search, AI-enriched product data and careful ranking can meet that expectation, but only on top of a catalogue with clean attributes. The unglamorous data work comes first.
Keyword, semantic or hybrid product search?
AI product search for e-commerce is not one technology. These are the main approaches, and most mid-sized catalogues end up with a hybrid.
| Keyword search | Semantic (vector) search | Hybrid with reranking | |
|---|---|---|---|
| Descriptive queries | Weak unless words match | Strong | Strong |
| Exact SKU, brand or model number | Strong | Can be unreliable | Strong |
| Setup effort | Low | Moderate | Higher |
| Merchandiser control | Synonyms and boosts | Harder to steer | Business rules on top of relevance |
| Running cost | Low | Embedding and hosting costs | Highest, usually still modest |
Hosted search products from your platform ecosystem are often the right answer. Custom work makes sense when catalogue size, languages or ranking logic outgrow them.
Where AI for e-commerce pays off beyond the storefront
Front-end features get attention, but many of the clearest returns for retailers come from the operational work behind each order.
Product data enrichment
Generate consistent attributes, descriptions and translations from supplier data and images, with human approval for anything customer-facing.
Order and returns assistant
Answer "where is my order" and start returns using live order data, handing complex complaints to your service team.
Demand forecasting
Forecast by SKU and location using sales history, promotions and seasonality to reduce both stockouts and clearance markdowns.
Supplier document processing
Read purchase orders, invoices and delivery notes from suppliers and match them against what was ordered and received.
Marketplace listing sync
Adapt listings to each marketplace format and keep stock and prices consistent across channels without manual copying.
Review and feedback analysis
Summarise reviews and support tickets by product to surface sizing, quality or description issues early.
Personalisation that helps rather than unsettles
E-commerce personalisation fails when it is launched before the data is ready or when it feels intrusive. A staged approach avoids both.
Fix catalogue data
Consistent categories, attributes and availability, because every recommendation model is only as good as the product data behind it.
Track the right events
Views, searches, add-to-carts and purchases captured reliably and with consent, across web, app and in-store where possible.
Start without personal data
Similar items, frequently bought together and trending in category deliver value from day one and solve the cold-start problem.
Personalise and measure honestly
Introduce user-level recommendations for logged-in shoppers and test against a holdout group, not against last year.
Platform, headless or custom: choosing retail architecture on evidence
For most retailers a mature commerce platform, such as Shopify or Adobe Commerce, is the right foundation. The problems we see usually sit between systems rather than inside them: inventory that drifts between the store, the ERP and the marketplaces, order management that relies on staff copying data, and point-of-sale data that never reaches the online team.
Headless commerce is worth it when your content, performance or multi-brand needs genuinely exceed what themes and apps can do. It also brings more code to own, more hosting to manage and more ways for peak traffic to find weaknesses. We will tell you plainly if a well-configured platform with a few custom integrations would serve you better.
Where we do build, AI-native engineering suits retail work well. Integration code for ERPs, marketplaces, payment and shipping providers is repetitive and heavily documented, which is exactly where AI coding agents save time. Senior engineers then focus on stock accuracy, checkout reliability and load testing before the big sales dates.
Questions we often hear
How does AI improve e-commerce search?
AI search uses language models to understand what a shopper means rather than matching exact words, so descriptive or misspelt queries still return relevant products. Combined with keyword matching for exact product codes and business rules for merchandising, it typically reduces zero-result searches and helps shoppers reach the right product faster.
How can retailers use AI without a data science team?
Start with AI features already built into your commerce, search or support platforms, and with operational tasks such as product data enrichment and order enquiries. Custom models become worthwhile once you have clean data, a clear metric to improve and enough volume for the gains to matter. An experienced partner can build and maintain them for you.
Do I need a lot of data for product recommendations?
Not to begin with. Recommendations based on product similarity and store-wide purchase patterns work with modest traffic. Personalised recommendations for individual shoppers need more interaction history, so smaller retailers should prioritise good catalogue data and non-personal recommendations first.
Is it safe to use AI to write product descriptions?
Yes, with review. AI produces consistent descriptions and translations quickly, but it can invent features, materials or claims that are not true. Generate from verified product attributes, block unsupported claims, and have a person approve content, especially for regulated categories such as cosmetics, supplements or children's products.
Should my online store go headless?
Only if you have a specific need that your platform cannot meet, such as highly customised content experiences, several brands on one backend, or performance limits you cannot solve otherwise. Headless adds development and hosting responsibility, so many growing retailers are better served by optimising their existing platform first.
Related reading and tools
Tell us where your store is losing sales or time.
Search that disappoints, systems that do not talk to each other, or an AI idea you want to test: describe it and we will reply within 24 hours with a practical view of where to start.
Working with companies globally · Response within 24 hours