AI for retail businesses that moves the metric you name.

A product recommendation engine analyses patterns in your transaction data to predict what a customer is likely to buy next. The core technique is collaborative filtering: customers with similar purchase histories tend to buy similar things, so the model uses the behaviour of similar customers to predict what the current customer will want. This is combined with content-based filtering, which recommends products similar to what the customer has already bought, and popularity signals, which ensure new or high-margin items get appropriate exposure. The model is trained on your historical transaction data: what customers bought, when, in what combination. It is updated on a schedule as new transactions come in. The output is a ranked list of recommended products for each customer, personalised rather than the same list for everyone. For online retail, this feeds the recommendation widget. For email marketing, it personalises the product selection in each send. For store operations, it informs product placement and cross-merchandising decisions. Recommendation engines typically lift basket size by 5-20% when personalisation replaces static featured-product logic.

Demand forecasting at the SKU and location level means predicting how much of each specific product will sell at each specific store or fulfilment location over a given time horizon. This is distinct from aggregate category forecasting, which is what most retailers have. SKU-location forecasting is harder because it requires the model to handle long tails of slow-moving SKUs, highly seasonal items with sparse history, and local demand differences that aggregate models smooth over. We use ensemble models that combine historical sales data with external signals: promotional calendars (planned promotions inflate demand and the model needs to account for them), local events, weather where relevant, and competitor pricing signals where available. Output is a daily or weekly forecast per SKU per location with confidence intervals. This feeds directly into your replenishment logic and purchasing decisions, replacing the spreadsheet-based forecasts that most retailers still rely on.

Customer churn prediction for retail works differently from subscription churn because customers don't formally cancel. Instead, they simply stop buying. The model learns to identify the behavioural signals that precede churn: declining purchase frequency, reducing basket size, last purchase recency crossing a threshold, shift from full-price to only promotional buying, and reduction in category breadth. These signals are combined with customer characteristics and segment membership to produce a churn probability score for each customer. Customers above a threshold score enter a retention workflow: a targeted offer, a personalised outreach, or a winback sequence, depending on the customer's value tier and the predicted reason for churn. The key design decision is the intervention threshold: if you intervene with too many customers, you discount customers who would have bought at full price anyway. We tune this threshold against your customer value distribution and promotion cost structure during build.

AI loss prevention in retail typically combines two capabilities. The first is transaction pattern analysis: the model analyses POS transaction data for patterns associated with employee theft or sweethearting, such as excessive voids, high refund rates on specific registers or shifts, transactions below average basket value on specific items, and timing anomalies. This runs on your existing transaction data with no additional hardware. The second capability is computer vision analysis of store camera footage: the model detects specific behaviours such as products being concealed, self-checkout anomalies, and high-traffic area patterns. This requires access to your camera feed and runs locally or via a secure cloud pipeline depending on your infrastructure. Most retail loss prevention AI implementations start with transaction analysis because it uses data you already have and delivers measurable results quickly. We assess which approach fits your data and operational setup during discovery.

Cost depends on the scope of the system and the state of your data. A focused single-model system such as a product recommendation engine or a churn prediction model typically runs between $30,000 and $80,000. A broader retail AI platform covering demand forecasting, personalisation, and dynamic pricing is typically $80,000 to $200,000. We provide a fixed-price quote after a 1-week discovery phase that maps your data, the specific model being built, and the integration work required. Price is locked before development starts and does not change unless the scope changes.

Yes. We sign mutual NDAs before any discovery conversation that involves sharing proprietary transaction data, pricing logic, or customer data structures. We have signed NDAs with clients in the US, UK, Europe, Canada, GCC, South Africa, and Southeast Asia. If your legal team has a standard NDA template, we will work from that. Our standard NDA covers transaction data, model architecture, and any business logic shared during the engagement.