Open-source LLMs for retail & e-commerce
Retailers use open models for product search, catalogue enrichment, customer support, returns handling and merchandising copy at high volume.
Where retail & e-commerce teams use language models
- Customer-support assistantlow risk
Orders, returns, delivery, sizing
- Catalogue enrichmentlow risk
Generate titles, attributes and descriptions from supplier data and images
- Conversational product searchlow risk
Natural-language shopping assistant
- Review summarisation & moderationlow risk
Summarise sentiment, flag policy violations
- Returns & claims processingmedium risk
Assess return reasons and photos
- Merchandising & campaign copylow risk
Multichannel copy in brand voice
- Supplier document processingmedium risk
Invoices, spec sheets, compliance certificates
- Store-associate assistantlow risk
Stock, policies, product knowledge on handhelds
- Demand & inventory narrative reportsmedium risk
Explain forecasts and anomalies to planners
Catalogue scale and seasonal peaks matter most here; the playbook asks about product-data formats, marketplace integrations and peak-to-average traffic ratios.
Frequently asked
Can an open LLM handle Black-Friday traffic?
Yes if sized for peak, not average. Enter your peak requests per second in the assessment; most retailers pair a mid-size model with autoscaled GPU replicas and cache common answers.