Search, recommendations and service at seasonal peak.
AI for Retail & E-commerce
Retail AI is judged on conversion and on holding up during the few days a year that actually matter.
What makes this sector different
Peak is orders of magnitude above baseline
Capacity planned for the mean fails on the day it counts.
Catalogue data is messy
Inconsistent attributes undermine search and recommendation.
Service volume follows sales
Contact volume peaks exactly when staff are stretched.
Where it pays back
Semantic product search
Retrieval-augmented generationUnderstand intent rather than matching keywords.
Personalised recommendations
Classical MLRecommend against live inventory and behaviour.
Customer service automation
HybridResolve order and returns questions from policy and order data.
Catalogue enrichment
Computer visionGenerate and normalise attributes and descriptions from images and copy.
How the Retail & E-commerce assessment differs
Retail assessments size for peak, not average — that is the number that determines the infrastructure bill.
Compliance we ask about first
Data sources we expect
Questions this template pushes on
- What is your peak-day traffic versus a normal day?
- How complete and consistent is catalogue attribution?
- Which service interactions may be fully automated?
These are suggestions surfaced alongside the questions — never pre-filled answers. The assessment still asks you everything, because a score built on assumptions you never confirmed is not a score you could act on.
Scope an AI project for retail & e-commerce
The Retail & E-commerce template sharpens the questions. Ten minutes gets you a feasibility score, an architecture and a costed plan.