AI development
for retail & e-commerce.
Retail has the shortest feedback loop of any sector, which makes it the best place to prove machine learning value quickly and the easiest place to optimise the wrong thing. Most of our work here is as much about choosing the right target metric as about the model.
Discuss your projectHigh-value use cases.
Search relevance
Usually the highest-return investment in e-commerce and consistently underinvested. Hybrid retrieval combining keyword matching with embeddings handles both exact product codes and vague descriptive queries.
Recommendations
Two-stage candidate generation and ranking across the catalogue. See recommendation engines for why offline metrics mislead and exploration is mandatory.
Demand forecasting
SKU-level prediction driving buying and allocation. Directly attacks the two biggest costs in retail: stockouts and markdowns.
Dynamic pricing
Elasticity modelling and markdown optimisation. Commercially powerful and needs guardrails — both for brand perception and for competition-law exposure.
Product data enrichment
Generating attributes, categories, and descriptions from images and supplier feeds. Fixes the catalogue quality problem that quietly caps search and recommendation performance.
Returns and fraud
Predicting return likelihood at the point of sale and detecting abuse patterns in refunds and promotions.
Sector-specific constraints.
Generic AI advice fails here for specific, predictable reasons. These are the constraints that shape every design decision we make in retail & e-commerce.
- Catalogue quality caps everything — poor product data limits search and recommendations more than any model choice
- Cold start is constant — new SKUs arrive continuously and have no interaction history
- Offline metrics mislead — logs only record outcomes for what the previous system chose to show
- Optimising clicks produces clickbait — pair engagement targets with retention, margin, and return rates
- Extreme seasonality — peak trading periods break models trained on typical weeks
- Latency is revenue — search and recommendation calls sit in the critical path of every page view
AI in retail & e-commerce.
What gives the fastest return in e-commerce AI?
Search relevance, in most catalogues we see. Site search users convert at several times the rate of browsers, so improvements land directly on revenue, and the baseline is often weak because default keyword search handles vague or misspelled queries badly. A hybrid approach combining lexical matching with embeddings, plus a reranking stage, typically produces measurable conversion gains faster than a recommendation project.
How do we stop our recommender from only showing bestsellers?
Popularity bias compounds because showing an item generates the interactions that justify showing it again. Counteract it deliberately: reserve a share of impressions for exploration, log what was shown rather than only what was clicked so you can correct for position bias, and track catalogue coverage and diversity as first-class metrics alongside click-through. Without explicit exploration, every retraining cycle narrows the assortment further.
Is dynamic pricing worth the risk?
It can be highly profitable and it carries real exposure that should be handled deliberately. Customer trust suffers if pricing appears arbitrary or personalised in ways people find unfair, and algorithmic pricing that converges with competitors' can attract competition-law scrutiny even without intent. The workable version uses guardrails — floors, ceilings, maximum change rates, and category exclusions — implemented outside the model where they are auditable and adjustable.
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ReadLet’s scope it
properly.
Tell us the problem you are trying to solve in retail & e-commerce and we will tell you honestly whether machine learning is the right tool.
Start the conversationor write to us at [email protected]