Welcome to Lang.ai! This guide will help you successfully set up and launch your first AI agent. Whether you've just installed the app or are planning to, this guide addresses the key steps and common challenges to get you up and running quickly.
🚀 Quick Start: Try the Demo Agent
Before diving into your own data, we recommend exploring our demo agent with synthetic data. This gives you a preview of what Lang.ai can do and helps set expectations for your results.
Access the demo agent: Available immediately after installation in your Lang.ai dashboard.
📚 Hands-On Tutorial
For a complete walkthrough from installation to first results, follow our step-by-step quick start. It's guided tutorial using sample data that covers installation, data setup, and agent creation.
➡️ Resource: Quickstart guide - See the entire process before working with your own data.
Understanding Lang.ai
Lang.ai transforms your Snowflake customer data into actionable insights through AI agents. Our agents analyze both structured and unstructured data to identify patterns, trends, and priorities that drive business outcomes.
Common use cases:
Retail: Decrease lapsed customers, increase first-time spend
Subscription: Boost conversion to paid plans, predict churn
Financial/Insurance: Connect feedback with financial activities
Healthcare: Improve onboarding, address side effects for retention
🔧 Pre-Installation: Security and Approvals
Before installation, your security team will need to review Lang.ai's approach to data handling and compliance.
➡️ Resource: Technical Overview and Security Features
⚙️ Installation Process
Choose the installation method that works best for your organization.
➡️ Resources:
Install via Snowflake UI - User-friendly interface approach
Install with SQL script - Manual installation for custom setups
Setting Up Access
After installation, configure user roles and permissions.
➡️ Resource: Setting up user access - Configure user roles and permissions for your Lang.ai app
📊 Preparing Your Data
The key to successful AI agents is well-structured data that combines customer interactions with relevant segments data. Segments are customer categorizations that enable deeper analysis (premium vs. standard, geographic regions, customer lifecycle stage).
Essential data components:
Customer interaction data (support tickets, surveys, chat logs)
Customer segments (plan type, signup date, revenue, etc.)
🤖 Creating Your First AI Agent
Lang.ai offers flexible agent creation to match your specific business needs. You can create custom agents focused on your particular use case rather than being limited to pre-built options.
Choosing your agent focus:
Retention analysis: Understand why customers cancel or churn
Sentiment analysis: Track customer satisfaction and feedback trends
Product feedback: Identify feature requests and pain points
Support optimization: Analyze support interactions for improvement opportunities
Custom focus: Define your own analysis parameters based on business goals
Agent configuration tips:
Be specific about your business goal when setting up the agent
Include relevant context about your customer base or industry in the Settings page
Consider the time period you want to analyze (recent trends vs. historical patterns)
➡️ Resources:
Granting table access to your AI Agent - Enable your AI Agents to analyze customer data by connecting your Snowflake tables
Creating your first AI Agent - Step-by-step guide to create your first AI agent.
Understanding Agent Processing
Processing time: Varies based on data volume, typically 30 minutes to 2 hours
What to expect: Your agent will analyze patterns, categorize feedback, and identify trends
Results format: Insights organized by categories with supporting evidence and metrics
📈 Working with Results
Interpreting Insights
Your AI agent results include:
Categories: Organized themes from your customer data
Trends: Changes over time with context
Segments: How insights vary across customer groups
Supporting evidence: Specific customer interactions that support each finding
Adding Deeper Analysis with Segments
➡️ Resource: Adding Segments to Gain Deeper Customer Insights - How to leverage customer segments for more actionable insights
Sharing Results
➡️ Resource: Connecting the Slack integration - Bring transparency by sending real-time insights directly to your team
🛠️ Troubleshooting and Support
Quick solutions for setup issues, data problems, and agent configuration challenges.
➡️ Resources:
Troubleshooting Agent Creation Issues - Quick diagnosis and resolution guide for AI agent setup problems
Troubleshooting Missing Data in Lang.ai - Why your data isn't showing up and how to fix it
Next Steps
Once your first agent is running successfully:
📊 Review results regularly: Check weekly insights and trends
🎯 Refine segments: Add more granular customer categorizations for deeper insights
🚀 Expand use cases: Create additional agents for different business questions
💼 Share with stakeholders: Use insights to drive data-informed decisions across your organization
Additional Resources
Enabling event and logs sharing with Lang.ai - Enable support features for your Lang.ai app
Sharing feedback on AI Agent insights - Learn how to help improve your AI agent's insights
Pricing and Billing - Understanding Lang.ai costs and payment structure
Need more help? Our team is here to support your implementation. Reach out by clicking on "Contact Support" below or get in touch with your Lang.ai representative.