Customer Service Automation · Enterprise Knowledge Search
Grainger, a leading North American distributor specializing in maintenance, repair and operations (MRO) supplies, manages a massive inventory of 2.5 million products that serve over one million customers. Grainger’s customer service team faced challenges in rapidly retrieving product information from their large catalog to support their high-traffic e-commerce website.
Reported impact
Grainger’s deployment of the Databricks Data + AI Platform dramatically reshaped their approach to managing an extensive product catalog. The impact of this integration has been profound, with significant advancements in search recall and discoverability across the company’s 2.5 million products.
Grainger built its Databricks implementation around Agent Bricks, Databricks Data + AI Platform, AI Search, Databricks Model Serving. Grainger, a leading North American distributor specializing in maintenance, repair and operations (MRO) supplies, manages a massive inventory of 2.5 million products that serve over one million customers. Grainger’s customer service team faced challenges in rapidly retrieving product information from their large catalog to support their high-traffic e-commerce website. Agent Bricks provides the agentic and generative AI runtime used to build domain-specific applications. Databricks Data + AI Platform provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workflows in this deployment. AI Search indexes enterprise data for semantic retrieval and RAG-style search experiences. Databricks Model Serving hosts production model endpoints or LLM interfaces used by applications and business users.
Source data is ingested into databricks, curated in shared lakehouse tables, processed with Databricks compute and orchestration services, and served through analytics, semantic search, or model endpoints for downstream users and applications. It connects with API.
400,000 Product changes handled daily
Explained Raghunathan, “We want to select the best component for each stage, from the best LLM that works with the task, to the best ETL to the best vector index. Having all this orchestrated within Databricks makes it very easy for me to d
Grainger wanted to implement an intuitive search system that enhanced the ability of call center agents to quickly and accurately support customer requests. According to Ranga Raghunathan, Director, Applied Machine Learning at Grainger, ”If
Their extensive product catalog of 2.5 million products was challenging for sales teams and customer service agents to navigate.
When it came to executing their e-commerce strategy, Grainger found itself in the same boat as many B2B distribution companies. Their extensive product catalog of 2.5 million products was challenging for sales teams and customer service age
With Databricks AI Search , Grainger had secure access to comprehensive data engineering and management features specifically designed to support RAG applications within a data intelligence platform. Stated Raghunathan simply, “We chose to
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