Buying team ordering stock based on last season's numbers because there's no forecast that accounts for current sell-through rate, location, and upcoming promotions?
High-value customers lapsing without warning because there's no early signal that purchase frequency is dropping before they stop coming back?
Retail AI and Analytics Software
Custom retail AI and analytics software for retailers who need to act on their data -- demand forecasting by SKU and location, personalised product recommendations, dynamic pricing, customer LTV and churn prediction, and performance dashboards that connect to your operational systems.
We build for retailers whose buying, commercial, and marketing teams are making decisions based on gut feel or spreadsheet exports because the analytics built into their current platform don't connect across channels or produce forecasts they can trust.
SKU-level demand forecasting by location and season to guide buying decisions before purchase orders are placed
Personalised product recommendations across e-commerce, email, and loyalty communications based on individual purchase history
Customer LTV and churn prediction that identifies your highest-value customers and flags at-risk ones before they lapse
Store and inventory analytics dashboards that connect data across POS, e-commerce, and loyalty without a separate BI stack
RaftLabs builds custom retail AI and analytics software for established retailers who need to act on operational data rather than just report on it. The software covers demand forecasting at the SKU and location level, personalised product recommendations, dynamic pricing based on inventory and demand, customer lifetime value and churn prediction, and store performance dashboards. A custom build is appropriate when the analytics built into your POS or e-commerce platform cannot connect across systems or produce the forecasts your buying and commercial teams actually use. Most retail AI projects ship in 12--14 weeks at a fixed cost.
100+Products shipped
·24+Industries served
·FixedCost delivery
·12-14Week delivery cycles
Most retail analytics shows what happened. It doesn't say what to do next.
The analytics built into Shopify, Lightspeed, and most POS platforms are reporting tools. They show sales by product, revenue by period, and traffic by channel. That information is useful but it arrives after the fact and within one system. The buying team needs to know what will sell next month at which location. The commercial team needs to know which customers are worth investing in through loyalty and which are at risk of not coming back. Those questions require forecasting, not reporting -- and forecasting requires data from more than one system.
Custom retail AI connects your POS transaction history, e-commerce orders, inventory positions, and loyalty data into a single model. The model can then predict demand at the SKU and location level, identify which customers are likely to buy again and which are drifting, and adjust prices automatically when a line is selling faster than expected or dead stock is building. The output is not a chart -- it is a buying recommendation, a customer re-engagement list, or a price change that the system applies without a commercial analyst running a spreadsheet.
The gap between a reporting dashboard and an AI-connected decision tool is not just technical. It requires someone to design what decisions the system is meant to support and what data is actually available. That design work is how we start every retail AI project -- before any model is built.
What we build
Demand forecasting
SKU-level demand forecasting that predicts what will sell, at which store location, over the next four to twelve weeks. The forecast uses your historical sales data, current sell-through rate, location-specific demand patterns, and known variables like upcoming promotions and seasonal shifts. Forecast output connects directly to your buying process: the system generates a suggested order quantity per variant per location that your buying team can review and approve rather than calculate from scratch. Accuracy improves over time as the model is retrained on new sales data after each season. Exception reporting flags where the forecast confidence is low -- typically new products without history or locations with unusual sales patterns -- so the buying team knows where human judgement should override the model. The demand signal that replaces last-season extrapolation with a forward-looking position.
Personalised product recommendations
Recommendations generated from each customer's individual purchase history, browse behaviour, and purchase frequency rather than from aggregate category popularity. Recommendations surface in the right place: on the e-commerce product page, in the post-purchase email, in the loyalty programme communication, and in the member portal. The recommendation model accounts for what the customer has already bought -- it won't recommend a product they purchased last month -- and for what is currently in stock and available in their preferred size or variant. For retailers with multiple store locations, recommendations weight towards products available at the customer's most-visited store. Recommendation performance reporting shows click-through rate and revenue contribution so the marketing team can see the financial return, not just the engagement metric.
Dynamic pricing
Automated price adjustments based on inventory level, rate of sale, and time remaining in the season. When a line is selling faster than forecast, the price increases to protect margin and slow depletion. When stock is building toward end-of-season, prices reduce to accelerate clearance before markdown becomes the only option. Pricing rules are configured with floor and ceiling thresholds per category so the system never prices below cost or above the recommended retail price. All price changes are logged with the reason and the inventory position at the time, so the commercial team has a full audit trail. The system can present proposed changes for human approval before they go live, or apply them automatically once the team is confident in the logic. The pricing tool that turns inventory pressure into a managed commercial decision rather than a panic markdown.
Customer lifetime value and churn prediction
LTV modelling that calculates the expected future revenue from each customer based on their purchase frequency, average basket size, category mix, and tenure. LTV scores let the marketing team prioritise retention investment: customers with high predicted LTV and no recent purchase get a different re-engagement message than customers with low LTV and declining frequency. Churn prediction identifies customers whose visit frequency has dropped below their personal historical baseline, flagging them for re-engagement before they stop returning entirely. The model distinguishes seasonal lapses -- customers who always buy around Christmas and then go quiet -- from genuine churn risk, so the marketing team does not burn re-engagement budget on customers who were never going to lapse. Output is a segmented customer list that feeds directly into your email or loyalty communications platform.
Store and inventory analytics
Performance dashboards for store managers and commercial leadership showing the metrics that drive daily and weekly decisions: sales against target by location, sell-through rate by category and style, days of cover by SKU, and conversion rate by store. Inventory turnover reporting identifies which lines are moving well and which are building dead stock before the end of season, giving the buying team time to act. Dead stock analysis shows the cost of held inventory and the projected markdown required to clear it, so the commercial team can make a clearance decision with the financial impact visible rather than guessing. Store ranking by performance metric across the estate lets the commercial director see which locations need attention and which are outperforming without asking each store manager for a report.
Loyalty programme analytics
ROI reporting that connects loyalty programme cost -- reward redemption value, campaign spend, points liability -- to the incremental revenue generated by loyalty members compared to non-members with similar purchase histories. Redemption rate by reward type shows which rewards drive the behaviour the programme is designed to encourage and which sit unused, so the marketing team can adjust the catalogue. Tier movement analysis tracks how members are progressing through tier levels, whether the tier thresholds are calibrated to achievable spend targets, and whether tier upgrades correlate with increased purchase frequency. Customer segment reporting breaks the loyalty base into cohorts by spend level, visit frequency, and category preference so communications can be targeted by segment rather than broadcast to all members. The analytics layer that turns loyalty programme data into decisions about programme structure, reward investment, and customer targeting.
Frequently asked questions
Demand forecasting needs at least 12 months of transaction-level sales data: date, store location, SKU or variant, quantity sold, and price. More history improves accuracy, particularly for seasonal businesses. The model benefits from product attribute data (category, style, colour, season code) so it can carry learnings from similar products when a new SKU lacks its own history. Promotional calendar data -- sale dates, price changes, marketing campaign dates -- is important because promotions are one of the biggest demand drivers and need to be separated from baseline demand in the model. We assess your data availability during project discovery and tell you what forecast accuracy to expect given what you have. Most retailers have enough transactional data in their POS history to produce useful forecasts; the work is extracting and cleaning it, not collecting more.
The analytics built into Shopify, Lightspeed, and similar platforms report on what happened within that platform. They show sales, revenue, and product performance for orders that flowed through the system. They do not combine data from your POS and your e-commerce platform into a single model, they do not forecast future demand, and they do not identify individual customers at churn risk. Custom AI is different in two ways. First, it connects data across all your systems into one model so the output reflects your full business, not one channel. Second, it produces decisions rather than reports: a buying recommendation, a customer segment list for re-engagement, or an automated price adjustment. If the analytics in your current platform are sufficient for the decisions you need to make, you do not need a custom build. If they are not, we scope what a custom build would deliver.
Both, depending on what makes sense for the project. For demand forecasting and pricing, the AI typically reads from your existing POS and inventory systems via API or scheduled data export, runs the model, and writes recommendations back to a dashboard or directly to your buying or pricing tool. For personalised recommendations, the output connects to your e-commerce platform and your email or loyalty communications platform. For customer LTV and churn, the output is usually a segmented list that feeds into your existing CRM or marketing tool. We avoid building a parallel analytics stack that your team has to maintain separately from the systems they use daily. The AI connects to where decisions are made, not to a separate portal that requires a separate login.
A demand forecasting tool with buying recommendation output typically runs $30,000--$70,000. Adding personalised recommendations, dynamic pricing, LTV and churn prediction, and a performance dashboard typically runs $70,000--$160,000. Cost depends on the number of data sources to connect, the complexity of your product catalogue and store estate, and whether the output integrates into existing tools or requires a new interface. We scope every project before pricing -- contact us with your current data setup, the forecasting or insight gaps you want to close, and what decisions you want the system to support. We'll give you a fixed cost.
