Retail & E-commerce

We build the models that move revenue and cut waste: personalized product recommendations, dynamic pricing tuned to demand and competitor moves, demand forecasting for inventory and restocking, real-time behavior tracking, and campaign optimization for seasonal peaks. Each one is scoped to your catalog and your numbers, not a generic plugin. The aim is higher average order value and conversion with fewer stockouts and less dead stock.

Yes. We integrate with Shopify, WooCommerce, Magento, and custom platforms, and we connect to your CRM, ESP, ad accounts, and data warehouse so models read live data and push decisions back into the tools you already run. Our team works with your developers on a clean deployment and keeps it compatible as your stack evolves.

Yes. Our models adapt recommendations to live browsing, cart, and purchase behavior rather than static rules, so a returning shopper sees relevant products in the moment. For storefronts that need millisecond responses at scale, we serve these models through real-time inference engines so personalization does not slow down the page.

We forecast demand at the SKU and location level from your sales history, seasonality, promotions, and price changes, then drive restock alerts and reorder points from those predictions. That balances availability against carrying cost. This sits on our demand forecasting work and can extend across your wider supply chain for upstream planning.

Yes. Dynamic pricing models adjust prices based on your rules, demand, competitor pricing, margin targets, and inventory levels, and they respect floors and ceilings you set so discounts never run away. You stay in control: the system proposes or applies changes within guardrails you approve, and you can keep a human in the loop for high-value SKUs.

Usually order history, product catalog and pricing, clickstream or behavioral events, inventory levels, and past campaign performance. The more history you have, the sharper the forecasts and recommendations. If your data is fragmented across the storefront, ad platforms, and a warehouse, we handle the consolidation, cleaning, and feature engineering as part of the build.

We validate on held-out data using metrics that match the goal, for example uplift and conversion for recommendations or MAPE for demand forecasts, and we only ship a model that beats your current approach. After launch we monitor for drift and retrain as buying patterns shift through seasons and trends, so accuracy holds instead of decaying. We do not quote a fixed accuracy number because it depends on your catalog and traffic.

A focused build such as a recommendation engine or demand forecasting model typically runs about 4 to 12 weeks across data preparation, training, validation, and integration. Multi-model systems that combine pricing, inventory, and personalization take longer. We give a firm timeline after an initial scoping and data audit.

Cost depends on scope, data readiness, and how many models and integrations you need, so we price it after the audit rather than quote a vague range. Budget for ongoing monitoring and periodic retraining as a yearly cost, since retail data drifts fast. You own the models and the logic, so you are not locked into a per-order vendor fee.

Brands we build for aim for higher average order value and retention from relevant recommendations, fewer stockouts and less overstock from better forecasting, stronger conversion from personalization, and more profitable, automated pricing decisions. We agree on the target metric up front and measure against your baseline so the impact is provable.