Retail & Marketplaces
E-Commerce & Retail.Product discovery that understands intent, not keywords.
Fine-tune on your catalogue, past search sessions, and return reasons to build search, recommendations, and product copy that speak your customers' language. Runs on your own infrastructure, so basket data and margins stay yours.
- Model weights, exportableYours
- Basket data sent to third partiesNoneWhen you self-host.
- Stored data encryptedAt rest
The problem
Keyword search misses intent, generic recommenders cannot reason about your merchandising rules, and sending catalogue plus behavioural data to a third-party API hands your margin structure to a vendor.
How Langtrain helps
Purpose-built for Retail & Marketplaces.
Intent-Aware Search
Train on real session logs so "something warm for a toddler in December" resolves to the right category, size and season — not a keyword soup.
Catalogue Copy at Scale
Generate titles, bullet points and meta descriptions in your brand voice for millions of SKUs, with tone locked to your style guide.
Attribute Extraction
Pull structured attributes — material, fit, compatibility — out of messy supplier feeds so filters and facets actually work.
Return-Reason Analysis
Cluster free-text return reasons to surface sizing and description problems before they become a margin leak.
Merchandising Guardrails
Hard rules on claims, pricing language and restricted categories are enforced at inference, not left to prompt discipline.
Localised Listings
Fine-tune once and generate market-appropriate copy across locales, respecting regional compliance wording.
Applications
What teams actually build.
- 01
Conversational Product Finder
A shopper describes the occasion; the assistant narrows to a handful of in-stock SKUs and explains the trade-offs between them.
- 02
Bulk Listing Enrichment
Ingest a supplier CSV and emit complete, on-brand listings with attributes, copy and category assignments ready for review.
- 03
Support Deflection for Orders
Answer "where is my order", sizing and returns questions grounded in your policy documents and live order status.
- 04
Review Summarisation
Condense thousands of reviews per SKU into an honest pros-and-cons block, with the caveats customers actually mention.
Check it first
Don't take our word for it. Test it on your own cases.
- 01
Hold back real cases
Keep 50 to 100 real examples out of the training data, with the answers you would accept.
- 02
Ask both models
Put the same questions to the base model and to your fine-tune, and read the answers side by side.
- 03
Have an expert review
Before anything ships, your merchandising team checks the answers and decides whether it is good enough.
How to run it: the quick start
No certifications yet: we have not completed a SOC 2 or ISO 27001 audit. For regulated data, train in your own environment.
Ready for Retail & Marketplaces?Start with your own data.
Talk to us, or start fine-tuning on the free plan.