This playbook shows how to use LangGraph workflows to generate campaign configs from simple inputs. We'll build a system that transforms product descriptions into complete campaign strategies with audience segments and ad variations, while preserving human approval before execution.
This playbook walks you through building an AI-powered cohort analysis system that automatically discovers which customer segments have the highest retention rates. The LangGraph pipeline dynamically analyzes multiple dimensions and delivers AI-generated insights for targeted retention strategies.
This playbook walks you through building an AI-powered CLV analysis system that reveals which customers drive long-term profitability. The LangGraph pipeline applies statistical models to predict behavior and delivers AI recommendations for optimization.
This playbook walks you through building an AI Flow that dissects ecommerce revenue drivers across a customer base. The LangGraph-based system breaks down top-line metrics into their component parts, identifies root causes, and delivers prioritized recommendations to improve performance
In this playbook, we will launch a personalization agent capable of generating revenue uplifts. The agent helps you map raw customer data to the recommended marketing experiences across email, website, and paid media.
This agent will help you increase customer revenue on the web, via email, and through paid media. It helps determine when to engage customers and how to best communicate with them.
