LangGraph
3 use cases using this technology
Unipol accelerates insurance operations with Generative AI built by Reply
Unipol Group
Italian insurer Unipol Group, serving customers through more than 2,000 agencies, worked with Reply (Cluster Reply and Sprint Reply) to build a governed Generative AI platform: a Foundation Layer/Unipol GenAI Stack using Azure AI Search and a Vector DB indexing over 60,000 documents; a Virtual Information Assistant chatbot with RAG over 50,000+ documents that reduces back-office ticket load and delivers responses in seconds; a network of AI agents built with Azure OpenAI, LangChain/LangGraph and UiPath that convert manual tasks into conversational interactions with real-time data validation; and an AI Guardrail governance layer (built on the LoopMind asset) providing LLM-as-a-judge monitoring, human-in-the-loop validation and AI Act compliance. Reply reports accelerated time-to-value, with new applications released in 1-3 months.
Fastweb + Vodafone: Transforming Customer Experience with AI Agents using LangGraph and LangSmith
Fastweb + Vodafone
Fastweb + Vodafone, part of the Swisscom Group and one of Europe's leading telecommunications providers serving millions of customers across Italy, built two AI agent systems on LangChain and LangGraph. Super TOBi transforms its existing chatbot TOBi into an agentic system using a Supervisor agent (for routing and guardrails) and specialized Use Case agents following the LLM Compiler pattern to call customer APIs and execute transactional actions like offer activation or payment updates. Currently serving nearly 9.5 million customers through the Customer Companion App and voice channels, Super TOBi achieves a 90% correctness rate, an 82% resolution rate, and a Customer Effort Score of 5.2 out of 7. Super Agent is an internal tool that augments call center consultants with diagnostics and guidance, storing operational knowledge as a graph in Neo4j and using an automated ETL pipeline with LangGraph and LLM agents to convert business-authored procedures into the knowledge graph; it has driven One-Call Resolution rates above 86%. The company uses LangSmith for observability, daily automated evaluation of chatbot responses, and continuous improvement.
How 7-Eleven, Inc. Built a Game-Changing GenAI Creative Assistant for Marketing with Databricks
7-Eleven, Inc.
7-Eleven's AI Center of Excellence built a multi-agent GenAI marketing assistant using LangGraph and Databricks, moving beyond early document-based chatbots and basic RAG. The system includes a Campaign Creative Generator, Copywriter Bot, General Assistant, and Supervisor Agent that orchestrates workflow and reviews outputs, with human-in-the-loop review, web search for trend intelligence, and guardrails filtering toxicity, PII and off-policy responses. Databricks provides governance and observability via MLflow tracking, automated agent evaluation, and inference tables. The assistant reduced manual creative hours, with campaign concepting and scripting that used to take hours or days now generated, refined, and approved in minutes; user feedback included calling it 'a game changer' and 'better than ChatGPT.'