Industry · Retail
AI Solutions for Retail and E-commerce
Demand forecasting, dynamic pricing and personalisation that survives peak trading.
Overview
Retail margins are decided by thousands of small, repeated decisions: what to stock, what to charge, what to show, when to discount. Each one is a candidate for a model, and each one compounds.
We build forecasting and pricing systems for retailers and marketplaces that work with real catalogue data, including the parts that are inconsistent, seasonal or sparse for long-tail SKUs. Recommendations arrive inside the merchandising or planning tool your team already uses, with the reasoning attached, so buyers can accept or override without leaving their workflow.
Peak trading is the real test. Everything we ship is load-tested for the volumes you see on your busiest week, with fallbacks that degrade to your current rules rather than failing.
Where AI pays off in Retail & E-commerce
Demand forecasting
SKU and store level forecasts that account for seasonality, promotions and cannibalisation, feeding replenishment directly.
Dynamic and competitive pricing
Price recommendations built from elasticity, competitor movement and margin floors, with guardrails you set.
Personalisation and search
Ranking and recommendation models tuned to your conversion metric, not a generic relevance score.
Catalogue and content generation
Product copy and attribute enrichment at catalogue scale, with human review on new categories.
What we design around
Retail & E-commerce constraints
- Long-tail SKUs with too little history for a naive model
- Promotional calendars and cannibalisation effects
- Peak-week traffic and latency budgets
- Hard margin floors and brand pricing rules
Services we apply here
How we build for Retail & E-commerce
Proof in production
Related case studies
Frequently asked
Retail & E-commerce: common questions
How accurate can demand forecasting get for long-tail products?
Sparse SKUs are forecast in groups rather than individually, borrowing signal from similar products. The realistic target is a meaningful improvement over your current planning baseline, which we measure before the build starts.
Will pricing recommendations break our brand rules?
No. Margin floors, MAP agreements and category rules are hard constraints in the optimiser, not suggestions. The model chooses only within the space you allow.
Where do recommendations appear for our merchandising team?
Inside the planning or merchandising tool they already use. Adoption drops sharply when people have to open a separate system to see a suggestion.
Let’s solve your data challenge