Industry Solutions

Retail & E-Commerce

Forecasting that survives a festival season, catalogue operations that do not need a small army, and customer service that resolves the query on the channel where it was asked.

Discuss Your Project

Industry Challenges

Forecasting through promotions, festivals and irregular demand

Catalogue and content operations consuming skilled time

Personalisation that stops at the recommendation widget

Service volume spiking at exactly the wrong moment

Channel data that never quite reconciles

Our Solutions

Demand forecasting with seasonality and promotion effects modelled explicitly

Catalogue enrichment and content generation with brand guardrails

Personalisation across the journey rather than one carousel

Customer assistants that can check an order, not merely describe the policy

Unified reporting across channels

What we build

Typical work in this sector

The systems we are most often asked to build in retail & e-commerce.

Demand forecasts that carry an interval, so planners know how much buffer to hold

Catalogue copy and attribute extraction at scale, reviewed before publication

Assistants handling order status, returns and exchanges end to end

Marketing automation through adrox, working from the same customer record

Our view

What we would tell you first

Retail forecasting fails at the edges — the promotion, the festival, the new product with no history. Those are exactly the periods that decide the year, so we spend our time there rather than improving the average week by a percentage point.

FAQ

Retail & E-Commerce Technology — Frequently Asked Questions

How do you forecast through festivals and promotions?+

By modelling them explicitly rather than treating them as noise. Promotions, festivals and new products with no history are exactly where naive forecasts fall over, and they are the periods that decide the year — so that is where we spend the effort, rather than improving the average week.

What can an AI assistant actually do for customer service?+

Resolve the common cases end to end — order status, returns, exchanges, delivery questions — by connecting to your systems rather than describing your policy. And hand over quickly to a person when it should, with the transcript and the order already loaded, because a fast handover is a good outcome rather than a failure.

Is personalisation worth it for a mid-sized catalogue?+

Sometimes not, and we will say so. With a small catalogue and thin behavioural data, good merchandising and search often beat a recommendation model. The honest test is whether you have enough traffic and history for the model to learn something a rule could not.

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Let's discuss how our technology solutions can address your specific industry challenges.

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