AI for E-Commerce

Modern store search is usually hybrid. Classic keyword matching is still the best tool for exact SKUs, brand names and model numbers, while semantic (vector) search understands that a shopper typing waterproof jacket for hiking wants products whose titles may never use those words. Blending both, then re-ranking results with signals such as stock, margin, popularity and the shopper's own behaviour, is what turns a search box into a sales channel. Zero-result queries become a to-do list for your catalogue team rather than a dead end.

Recommendations follow the same pattern: collaborative signals (people who viewed this also bought), content similarity for new or long-tail products with little history, and business rules your merchandisers control - pin a campaign, exclude out-of-stock items, avoid recommending a cheaper substitute on a product page. We keep those controls in your team's hands so the model never overrides a commercial decision you have made.

Most AI search and recommendation problems are really catalogue problems: missing attributes, inconsistent sizing, thin descriptions and supplier data in a dozen formats. Large language models are well suited to fixing this at scale - extracting structured attributes such as material, fit, compatibility or dimensions from supplier text and images, normalising values to your taxonomy, writing descriptions in your brand voice, and translating listings for new markets.

The risk is confident invention, such as a model stating a fabric or a spec that is not in the source data. We constrain generation to the facts supplied, flag low-confidence fields, and route new or changed content through a review queue before it goes live, so speed does not come at the cost of returns, complaints or misleading claims.

Forecasting models combine sales history with seasonality, promotions, price changes, lead times and external signals to predict demand per SKU and location. The useful output is not a single number but a range, which feeds reorder points and safety stock so buyers can weigh the cost of a stock-out against the cost of tying up cash in slow inventory. New products with no history borrow patterns from similar items.

Support assistants are most valuable when they are grounded in your real systems: order status, delivery tracking, return eligibility and product data. An assistant that can answer where is my order or will this fit my model from live data resolves common questions instantly and hands off to a human, with the full conversation, when it reaches a refund dispute or anything outside its policy.

AI features should earn their place through controlled experiments, not before-and-after impressions that confuse the change with seasonality or a marketing push. We define the success metric up front - conversion rate, revenue per session, average order value, search exit rate or contact rate - and run A/B tests with a holdout group long enough to reach a reliable result, then keep monitoring after launch because models drift as catalogues and shoppers change.

Data readiness usually decides the timeline. Before modelling we check that product data is complete, that behavioural events (views, add-to-cart, purchases, searches) are tracked consistently and tied to sessions, that order and returns history is accessible, and that consent and privacy settings permit the intended use. Where those foundations are weak, fixing them is the first milestone - and it improves your analytics even before any model ships.