THE INDUSTRY

AI Across the Retail Chain

Retail and ecommerce generate more structured behavioural data than almost any other sector. Most of it is underused. AI changes that across the entire chain. Forecasting models predict sales at SKU level, feeding inventory planning systems that reduce stockouts and the lost revenue they cause. Recommendation engines surface products based on browsing behaviour, purchase history, and real-time session signals. On the operations side, NLP models extract structured insight from customer complaints at scale, identifying product defects and recurring friction points that inform product prioritisation. LLM-powered agentic workflows handle customer service across channels and languages, with RAG pipelines grounding responses in real product data and policy documents. The organisations that connect these systems, from recommendation to fulfilment to post-purchase, compound the advantage at every stage.

Demand forecasting and inventory planning

ML models that predict demand at SKU level across product ranges, channels, and time horizons. AI sales forecasting that feeds directly into inventory optimisation, reducing stockouts and the revenue they silently destroy. Built to handle real transaction volumes, seasonal patterns, and promotional spikes.

Customer service automation with LLMs

LLM-powered agentic workflows that handle customer enquiries across channels and languages. RAG pipelines grounding every response in real product data, order history, and policy documents. AI customer service automation that resolves issues rather than deflecting them, with human escalation built in where it matters.

Personalisation and recommendation systems

AI recommendation engines that surface products based on browsing behaviour, purchase history, and real-time session context. Ecommerce personalisation that increases basket size and conversion without relying on blanket discounting. Models trained on actual transaction data, not assumptions about what customers want.

Product intelligence and analytics

NLP pipelines that extract structured insight from customer reviews, complaints, and returns data at scale. Automated detection of product defects, sizing issues, and recurring friction points. Intelligence that feeds directly into product prioritisation and design decisions, closing the loop between what customers say and what gets built next.