{"slug":"lippert-improves-customer-support-with-genai","url":"https://findausecase.com/use-cases/lippert-improves-customer-support-with-genai","title":"Lippert Improves Customer Support with GenAI","description":"Lippert, a $3.8 billion global manufacturer of components for RV, marine and automotive brands, used Databricks and Agent Bricks to consolidate fragmented data into a single lakehouse and deploy AI agents for customer support. An AI assistant trained on product manuals, technical case history and field-expert videos surfaces troubleshooting guidance for call center agents in real time, cutting new-agent ramp time from six months to four weeks (an 85% reduction). Using Agent Bricks' evaluation framework and synthetic data, model accuracy for the support agent improved from 33% to 84% within weeks. AI also analyzes thousands of support calls daily for coaching and quality scoring (versus a prior manual sample of 100 calls/month) and automates call summarization into Salesforce. The initiative is expected to save millions per year and reclaim hundreds of thousands of hours, with new agents planned for HR, warranty and supply chain.","company":"Lippert","industry":"Manufacturing","aiCapabilities":["Retrieval-Augmented Generation","Conversational AI","Natural Language Processing"],"technology":["Databricks","Agent Bricks","Unity Catalog","MLflow","Delta Lake","Databricks SQL","AI/BI Genie","Databricks Assistant"],"deployment":"Public Cloud","problemStatement":"As demand for mobile living surged, call volumes at Lippert's call center spiked into the millions and legacy tools like Synapse and Dynamics 365 couldn't scale, leaving fragmented systems, slow onboarding (up to six months for a new support agent) and limited insight into service quality.","solutionApproach":"Using Databricks and Agent Bricks, Lippert consolidated fragmented data into a single lakehouse and built an AI assistant trained on product manuals, technical case history and field-expert videos that surfaces real-time troubleshooting guidance for call center agents. Using Agent Bricks' evaluation framework, the team generated a synthetic dataset of 50 question-answer pairs to test and improve retrieval and chunking before involving subject matter experts. AI also analyzes thousands of support calls daily for coaching and quality scoring, and automated call summarization pushes case summaries directly into Salesforce.","businessValue":"The AI assistant cut new-agent ramp time from six months to four weeks, an 85% reduction. Using Agent Bricks' synthetic data and evaluation loop, model accuracy for the support agent improved from 33% to 84% within weeks (33% to 56% from synthetic data alone, with SME input from around 54%). Lippert's data team saw a 40% increase in productivity, and the company expects to save millions per year and reclaim hundreds of thousands of hours by the end of 2025. The company also delivers key business insights 43% faster than before.","evidence":{"band":"high"},"sourceUrl":"https://www.databricks.com/customers/lippert","dates":{"publishedAt":"2026-08-15T23:34:19.626Z","publishedAtSource":"ledger","updatedAt":"2026-08-26T08:09:17.656Z"},"license":"Open for reading and citing with a link to https://findausecase.com/use-cases/lippert-improves-customer-support-with-genai. Bulk republication requires permission."}