AI automation for ecommerce, with the payback stated up front

The realistic uses are narrow and specific: drafting product content from real spec data, answering routine customer questions from your own policies, routing orders and flagging exceptions, syncing data between systems, and summarising performance. Each replaces defined repetitive work. AI does not improve a store's strategy or fix a product nobody wants.

Start with internal, invisible work where a mistake is caught before a customer sees it: product description first drafts, inventory sync, and order routing. These pay back immediately and fail safely. Customer-facing automation like a support assistant is higher effort, slower to pay back, and expensive when it gets something wrong.

Sometimes, and less than vendors claim. Recommendations work when a catalogue is large enough that customers cannot browse it and varied enough that relevance differs by customer. On a 40-product catalogue, a well-organised collection page usually outperforms a recommendation engine. We look at catalogue size and browse behaviour before recommending them.

Yes, if the model is given real product data and a house style, and every output gets a human pass before publication. Descriptions generated from a product name alone produce plausible but inaccurate claims about a physical item, which is a commercial and legal problem before it is an SEO one. Review is not optional.

Build cost depends on whether it answers from your own content, which requires a retrieval system, or from generic knowledge, which is rarely useful. Running cost is per-conversation and ongoing. Before quoting we ask how many support tickets you receive and what fraction are genuinely routine, because that number determines whether it pays back at all.

It depends entirely on what gets sent where, which is why we document it before building. We minimise personal data sent to any model, build retrieval over content you control, and confirm each provider's retention and training terms in writing during scoping. As data controller those obligations remain yours; we make sure your records are accurate.

Retrieval-augmented generation means the system searches your own content first, then asks the model to answer using what it found. Your store cares because it is the difference between an assistant that quotes your actual returns policy and one that invents a plausible policy. For any customer-facing use, retrieval is the requirement.

Usually without a language model at all. Order routing and inventory sync are rules and API calls: a webhook fires on order creation, logic decides the destination, and systems update. Shopify Flow and WooCommerce's Action Scheduler handle much of this natively. We use a model only where the task genuinely needs judgement.

Pricing decisions, final customer-facing copy without review, anything with legal or safety consequences, fraud decisions on model output alone, and support escalation — a customer asking for a human should get one immediately. These fail in ways that cost more than the automation saves, and the failures are public.

Agree the measure before building: hours saved per week, tickets deflected, or error rate against the manual baseline. Record the baseline first. That number is what defends the automation the first time it gets something wrong, which it will. Automations without a measure get switched off after the first visible failure.