AI for ecommerce operators —
Shopify, Amazon, fashion, wholesale and more.

AI for ecommerce is the application of machine learning to operational decisions — inventory, pricing, recommendations, and returns — using a retailer's own transaction data. Unlike generic SaaS tools, custom AI is trained on your specific catalogue, buyer behaviour, and seasonality, and runs inside your existing stack. For a Shopify brand, that might mean a demand forecasting model that predicts stockouts two weeks out. For an Amazon FBA seller, it might mean a restock timing model that minimises storage fees without running dry.

AI improves ecommerce performance in three ways: it reduces costly operational errors (stockouts, overstock, mismatched payments), it personalises the buying experience (recommendations, dynamic pricing), and it automates work that currently takes hours each week (reconciliation, reorder decisions). Across the operators we work with, the biggest early wins come from demand forecasting — stockouts drop 30–50% — and recommendation engines, which typically lift average order value 15–30% within 60 days of launch.

Shopify and WooCommerce are the most common. We also work with Amazon Seller Central (FBA and FBM), Magento, and custom stacks. Data warehouses: Snowflake, BigQuery, Redshift, and direct database access. Accounting: QuickBooks, Xero, and NetSuite. If you're on a platform not listed, tell us on the call — we've integrated with most stacks in the $1M–$20M range.

For most Shopify and wholesale operators, that means demand forecasting — the ROI is measurable within one season and the data requirements are usually already met. For businesses with strong repeat-purchase behaviour, a recommendation engine typically returns faster. The right answer depends on your margin profile, data quality, and where you're currently losing the most money — we scope that on the first call.

A SaaS tool is a generic model trained on data from many businesses. It can get you part of the way, but it plateaus because it doesn't know your specific catalogue, customers, or seasonality. Custom AI is trained only on your data, runs in your stack, and you own it outright — no monthly fee, no vendor dependency. At $1M–$20M, that difference in signal specificity is usually worth 20–40% better forecast accuracy.

Yes. Both need demand forecasting, but the data structures and success metrics are different. FBA sellers optimise for restock timing against fulfilment centre lead times and storage fee thresholds. Shopify sellers optimise for AOV, return rate, and reconciliation accuracy. The same model architecture won't serve both well — we build for the specific platform, not a generic ecommerce template.

For inventory forecasting, typically 12–24 months of order history and current stock levels. For recommendations, transaction history and product catalogue data. For returns prediction, order-level return records with at least 6 months of history. We assess data readiness before committing to any milestone — if gaps exist, we either include a data-cleaning step in scope or recommend addressing infrastructure first. You don't need perfect data to start, but we'll tell you exactly what you need on the first call.

Engagements typically run between $12,000 and $60,000 per milestone, depending on data complexity, the number of integrations, and the model type. A single Shopify inventory forecasting implementation is toward the lower end. A multi-system recommendation engine across Shopify, a data warehouse, and an email platform is toward the higher end. We price by milestone, not by hour — you know the exact cost before work starts.

Fashion has stronger seasonality, a size-curve problem that most generic models ignore, and higher return rates with more explainable root causes. The data structure is also more complex: the same style in different colours and sizes behaves as distinct demand signals that need to be modelled at the variant level, not the product level. We build seasonal forecasting models that account for newness curves, markdown cadence, and size-ratio optimisation specifically for apparel — not repurposed grocery demand models.

Health and wellness brands typically have two overlapping demand patterns: subscription (predictable, churn-driven) and one-time purchase (harder to forecast). The AI challenge is modelling both simultaneously and forecasting net inventory need after accounting for expected churn, skips, and new subscriber cohorts. Reorder automation also matters more here because running out of a hero consumable SKU has immediate subscriber retention consequences, not just a lost sale.