AI for e-commerce, built to move one number you choose.

A personalised product recommendation engine analyses the patterns in your transaction data to predict what a customer is likely to buy next. The primary technique is collaborative filtering: customers with similar purchase histories tend to buy similar products, so the model uses the behaviour of similar customers to generate recommendations for the current customer. This is combined with content-based filtering, which recommends products similar in attributes to what the customer has previously bought, and popularity signals that ensure new or high-margin products get appropriate visibility. The model is trained on your historical transaction data and updated on a rolling schedule as new purchases come in. Output is a ranked recommendation list for each customer: next purchase prediction, cross-sell candidates, upsell opportunities, and replenishment timing for consumable products. For online retail, this feeds your recommendation widgets, email product selections, and paid retargeting campaigns. For marketplaces, it personalises the search result ranking and homepage product surfaces for each logged-in buyer.

Dynamic pricing for e-commerce uses demand signals, inventory levels, competitor pricing, and margin constraints to recommend an optimal price for each product at each point in time. The model monitors how conversion rate and units sold respond to price changes for each product, learns the price elasticity of demand in your catalogue, and recommends prices that maximise revenue or margin given your inventory position and competitive context. For products where demand is highly elastic, commoditised items with many competitors, the model keeps prices competitive. For products where demand is inelastic and inventory is constrained, exclusive products or limited-run items, the model captures more margin by pricing higher when demand is strong. You define the price floors, brand positioning rules, and margin minimums. The model optimises within those constraints. For marketplace operators, dynamic pricing models also feed the buybox competition logic. We assess your price history and competitor data access in discovery.

E-commerce churn prediction works differently from subscription churn because customers don't formally cancel, they simply stop buying. The model learns to identify the behavioural signals that precede churn in your transaction data: declining purchase frequency, lengthening inter-purchase intervals, falling average basket value, a shift from full-price purchasing to buying only on promotion, and reduction in the number of product categories bought. These signals are weighted by customer value tier and combined into a churn probability score. Customers above a threshold score enter a retention workflow: a targeted offer, a personalised email sequence, or a loyalty programme prompt, calibrated to the customer's predicted lifetime value and the estimated cost of the incentive needed to retain them. The key decision is the intervention threshold: if you discount too many customers, you reduce margin on customers who would have bought at full price. We tune this threshold against your customer value distribution and promotion cost structure during scoping.

AI search in e-commerce uses vector embeddings and semantic similarity to return relevant results even when the customer's search query doesn't exactly match product attribute text in your catalogue. A customer searching for 'summer work outfit' returns clothing items that match the concept rather than only products that contain those exact words in their description. The search model learns the semantic relationships between customer language and product attributes from your query-click-purchase data: which queries led to which products being clicked and bought. This is combined with personalisation signals so that the results ranked highest for a returning customer reflect their past purchase and browse behaviour, not just catalogue-wide popularity. For large catalogues, AI search also powers the autocomplete and query suggestion layer, surfacing popular and high-conversion search terms as the customer types. We assess your product catalogue size, current search infrastructure, and query-click data in discovery to determine the integration approach.

The cost depends on the scope: a single AI capability such as a churn prediction model or demand forecasting layer typically runs between $30,000 and $80,000. A full AI stack covering recommendations, dynamic pricing, search, and fraud detection is a larger engagement. Every project starts with a fixed-price discovery phase where we map your data, define the outcome metric, and produce a written quote before development begins. You know the cost before any code is written.

Yes. A voice agent authenticates the caller by phone number or order ID, queries your order management system in real time, and delivers accurate status or walks the customer through return eligibility, reason collection, and label generation, all within a single call under 60 seconds. Integration is via the Shopify Admin API, WooCommerce REST API, or your OMS, with refunds routed to a human review step for high-value or out-of-policy cases. A mid-market retailer we worked with handled 71 percent of inbound calls without escalation during its first major sale event after deployment, and skipped seasonal temp hires entirely.

Most single-capability AI builds go from kick-off to production in 10 to 14 weeks. Multi-capability engagements covering recommendations, pricing, and fraud detection together typically take 16 to 20 weeks. The timeline depends on the state of your transaction data and the complexity of your e-commerce platform integrations. We deliver a working system at a staging URL by the end of sprint one, usually 2 to 3 weeks into the project, so you can see progress early and adjust scope before the build is complete.