customer_support · ecommerce · workflow
Grainger uses Databricks Mosaic AI and RAG to power product search across 2.5 million products
Grainger's 2.5 million-product catalog was difficult for sales teams, call center agents, and non-specialist customers to navigate, while over 400,000 daily product updates made real-time accuracy hard to maintain with their existing search infrastructure.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Customer or agent submits product query
A customer calls in or searches the e-commerce site with a product inquiry, potentially without knowing the technical specifications.
Tools used
DatabricksDatabricks Mosaic AIVector SearchModel Serving
Outcome
Grainger achieved significant advancements in search recall and discoverability, with sales teams and call center agents equipped with faster and more accurate product retrieval, saving time and reducing errors.
Results
Time savedsaves time
Volumesignificant advancements in search recall and discoverability
Grounding & classification
Source type: vendor customer story
27 fields verified against source quotes.
conversational aienterprise searchragknowledge baseproduct catalognamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedretailaccuracy improvementcustomer satisfactionemployee productivityvendor customer storycustomer supportecommerce opssales opsdata sync enrichmentrag answering