Use Cases
Build a Semantic Model with AI
Use an AI coding agent to create and manage your semantic model through natural conversation — all without leaving your IDE. For example:- Bootstrap a new semantic model in minutes by importing warehouse tables and defining entities, relationships, and metrics
- Export semantics from an existing semantic model, query, or data source
- Quickly add and validate new metrics and attributes
- Ask data questions and iteratively add and refine missing semantics
Deep Analysis
Use the deep analysis capability for multi-step analytical workflows. The AI agent breaks down complex questions, runs multiple queries, and synthesizes the results into a coherent answer. For example:- “Why did revenue drop last quarter? Break down by region and product category.”
- “Compare customer retention rates across acquisition channels over the past year.”
- “Which product lines are underperforming relative to their growth targets, and why?”
- “There was a spike in returns last week. Identify the root cause and affected segments.”
Embed AI Analytics in Applications
Build AI-powered data applications using the Honeydew AI API. The API supports natural language questions, multi-step deep analysis, and follow-up conversations — all grounded in your semantic model. You can integrate Honeydew AI into:- Custom chatbots and assistants
- Internal data portals
- Slack and Teams workflows (Slack App, Teams App)
- Streamlit dashboards (Streamlit)
Getting Started
To start building with AI:- Connect an AI coding agent — Set up the MCP Server in your preferred client (Claude Code, Cursor, VS Code, and more)
- Install plugins — Add the Honeydew plugins for guided semantic modeling and data analysis workflows
- Explore your data — Ask questions, build your model, and query data through natural conversation