Generative AI development in e-commerce
Retailers have large catalogs, repetitive customer questions, and constant demand for product content. Generative AI fits where it reads catalog and order data and answers from it.
Where generative AI fits
- Conversational product search that maps a shopper’s description to catalog items.
- A service assistant that answers order, return, and product questions from your policies and order data.
- Drafting product descriptions and enriching attributes from supplier data, for review.
- Summarizing reviews for shoppers and merchandisers.
What shapes the build
Freshness
Prices, stock, and listings change daily. Retrieving from live data keeps answers current, where training facts into a model would not.
Facts from the catalog
The model should not invent specifications, prices, or policy. Answers should come from catalog fields and be checked against them.
Claims in generated copy
Product copy for regulated or claim-sensitive categories needs human review before it is published.
Customer data access
An assistant that reads orders must show each customer only their own.
What we ask first
- Which questions do customers ask most, and what data answers them?
- How often does the catalog change, and where does the source of truth live?
- Which product categories carry claims that need review?
- Which systems does the assistant need to read, and which may it change?
Services that apply
This page describes the field. We have not published a case study in e-commerce yet. The case studies page lists the work we have.