Sending the same promotion to every customer, stocking what sold last year rather than what will sell next quarter, and discovering payment fraud after a chargeback arrives: these are the margin and revenue problems that AI addresses in e-commerce. We build AI systems for e-commerce retailers, marketplace operators, and DTC brands: personalised product recommendations, dynamic pricing, demand forecasting, customer churn prediction, AI-powered search and discovery, review analysis and sentiment monitoring, fraud detection for payments and chargebacks, and AI customer support for order queries. Every system is scoped against your transaction data and a specific revenue or cost outcome.
Recent outcomes
Voice AI · Research
Text-based interviews converted to automated phone calls
6× deeper insightsAI Automation · Ops
Manual invoice OCR across 40+ gas stations
20k+ txns day oneLoyalty · Retail
SuperValu & Centra loyalty platform with receipt validation
1,062 users in 4 weeksSaaS · Logistics
Multi-carrier shipping hub for Indonesian eCommerce
2,000+ shipments yr 1RaftLabs builds AI systems for e-commerce retailers, marketplace operators, and DTC brands including personalised product recommendation engines trained on your transaction history, dynamic pricing models, SKU-level demand forecasting, customer churn prediction, AI-powered search and discovery, review sentiment analysis, and fraud detection for payments and chargebacks. Personalised recommendation engines typically increase basket size by 15--25%. Each engagement is scoped at a fixed price after a discovery phase that maps your transaction data to the specific AI capability being built.
Trusted by
The data most e-commerce businesses need to improve personalisation, reduce inventory waste, and retain customers already exists in their transaction history, customer records, and product catalogue. The gap is between collecting that data and using it in real-time decisions. AI closes that gap.
Capabilities
Recommendation models trained on your transaction history using collaborative filtering and content-based approaches. Personalised next-purchase, cross-sell, upsell, and replenishment recommendations for each customer. Output feeds your recommendation widgets, email product selections, and retargeting campaigns. Updated on a rolling schedule as new transactions come in -- not a static product list applied to all customers. Trained on your catalogue and purchase data, not a generic retail model.
Pricing models that respond to demand signals, inventory levels, and competitor pricing within your defined price floors and margin rules. Learns price elasticity per product from your historical conversion and revenue data. Recommends prices that maximise revenue or margin depending on your inventory position and competitive context. Outputs recommendations through a merchandising dashboard or pushes directly to your e-commerce platform via API. Built with full explainability so your team understands each price recommendation.
SKU-level demand forecasts trained on your sales history, promotional calendars, and external signals. The core forecasting layer combines gradient boosting models (LightGBM, XGBoost) for tabular sales history with time-series methods (Facebook Prophet, LSTM) for capturing seasonal patterns and long-range trends. For products with limited history -- new launches, reintroductions -- the model uses category-level and attribute-level signals to cold-start the forecast rather than defaulting to zero. Promotional lift factors are learned from past campaign data and applied to forthcoming promotional windows so the forecast reflects planned activity, not just organic demand.
Accounts for seasonality, promotional lift, and new product introduction. Forecasts feed your replenishment and purchasing decisions, replacing spreadsheet-based projections. For marketplace operators, demand forecasting also informs seller inventory recommendations and warehouse allocation. Output is a point forecast and confidence interval per SKU per time period, giving your buying team both the central estimate and the uncertainty range needed to make stock decisions. Reduces both overstock carrying costs and lost sales from stockouts by matching stock levels to model-predicted demand rather than historical averages.
Classification models trained on your customer transaction history that score each customer by churn probability based on purchase frequency trend, inter-purchase interval, basket value trajectory, and category engagement.
The modelling approach combines RFM (Recency, Frequency, Monetary) feature engineering with gradient boosting classifiers (XGBoost, LightGBM) that incorporate additional behavioural signals: the share of purchases made on promotion, the number of active product categories, browse-without-purchase sessions, and support contact frequency. For businesses with longer purchase cycles (furniture, appliances, seasonal categories), survival analysis models calibrate the expected inter-purchase interval per customer segment so the churn signal is relative to that customer's normal rhythm rather than an absolute time threshold applied to everyone.
High-risk customers enter a retention workflow before they stop buying, not after. The intervention logic segments customers by both churn risk and predicted customer lifetime value (CLV) so the response is proportionate: high-CLV, high-risk customers get a direct outreach or personalised offer; low-CLV, high-risk customers get a lower-cost email sequence or are suppressed from paid retargeting. Suppresses promotional spend on low-risk customers who will buy at full price anyway. Intervention threshold is tuned against your customer value distribution and promotion cost structure to maximise retention ROI rather than maximising retention volume.
Semantic search using vector embeddings that returns relevant results even when customer query language doesn't match product attribute text. Trained on your query-click-purchase data to learn which products customers actually buy when they search each term. Personalised result ranking for returning customers based on purchase and browse history. Autocomplete and query suggestion powered by high-conversion search patterns. Applicable to on-site search, catalogue navigation, and recommendation surfaces across your e-commerce platform.
NLP models that process customer reviews, support tickets, and return reasons to extract structured sentiment signals: which products generate complaints, which complaints are trending, which specific attributes -- fit, quality, delivery, packaging -- drive satisfaction or dissatisfaction.
The pipeline uses transformer-based text classification (fine-tuned on your review corpus) for aspect-level sentiment extraction, identifying not just whether a review is positive or negative but which product dimension the sentiment is attached to. Topic modelling (LDA, BERTopic) surfaces emerging complaint clusters before they accumulate enough volume to appear in aggregate ratings. Named entity recognition links specific complaints to SKUs, supplier batches, or shipping carriers rather than leaving them as undifferentiated review text.
Tracks sentiment trends over time so your product and operations teams can see whether a complaint theme is growing, stabilising, or resolving after a corrective action. Integrates with your review platform API (Bazaarvoice, Yotpo, Trustpilot, or direct database export) and sends alerts when a new negative theme crosses a configurable volume threshold. Gives you a continuous, structured signal from customer language rather than a quarterly aggregated star rating that tells you something is wrong but not what or where.
Basket size, churn rate, stockout rate, or fraud losses: tell us the number and we will assess which AI system addresses it and what it costs to build against your data.
We build fraud detection models for e-commerce transactions that score each order by fraud probability using card data, device fingerprint, order characteristics, and velocity signals. High-risk orders are flagged for review before fulfilment. Trained on your historical transaction and chargeback data. Reduces chargeback rates without increasing false declines on legitimate orders. For marketplace operators, the same model architecture also detects seller-side fraud patterns such as fake reviews and listing manipulation.
Digital Commerce industry context -- AI and digital commerce industry overview
Ecommerce Automation -- automation for e-commerce operations and workflows
Recommendation System Development -- recommendation engines across industries
Predictive Analytics -- demand and behaviour forecasting models
AI for Retail -- AI systems for retail businesses including in-store operations
AI Development -- end-to-end custom AI system builds
Frequently asked questions
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-powered 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.
Work with us
We scope AI for E-Commerce in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.