Product-catalogue enrichment with Gemini on Vertex AI
How a retailer could generate consistent product attributes and descriptions from images and supplier data using Gemini’s multimodal capabilities.
The situation
An online retailer onboards thousands of new products each season. Supplier data is incomplete and inconsistent, so merchandisers spend hours writing descriptions and filling attributes, delaying launches and hurting on-site search.
Our approach
Define the attribute schema
Agree required attributes, allowed values and style guidelines per category.
Multimodal extraction
Gemini on Vertex AI reads product images and supplier specs to propose attributes and flag conflicts.
Generate descriptions
Gemini drafts on-brand descriptions grounded only in verified attributes.
Merchandiser review
Merchandisers approve or edit in a review queue; edits feed back into prompts and evaluation.
Improve search
Enriched attributes power Vertex AI Search and filters on the website.
Typical technology
What success looks like
Metrics we would agree with you up front and track throughout:
- Time to publish a new product
- Attribute completeness
- Share of descriptions approved without edits
- On-site search conversion
- Returns due to inaccurate descriptions
This is an illustrative scenario showing how Inspired Infotech approaches this type of problem. It does not describe a specific client, and actual approach and outcomes depend on each organisation’s systems, data and goals.
Services behind this scenario
Google Vertex AI & Gemini
Gemini models, Vertex AI Search and agents on Google Cloud — multimodal AI grounded in your data.
Learn moreGenerative AI
LLM-powered assistants, RAG search and content automation — secure, grounded and enterprise-ready.
Learn moreGoogle Cloud
Data, analytics, Kubernetes and Gemini-powered AI on Google Cloud Platform.
Learn moreFacing a similar challenge?
Let’s talk about your situation and what a realistic plan looks like.