Use Cases
One Model. Wherever You Start.
Start with AI context, a data warehouse rebuild, or a new data product. Ellie keeps one model underneath all three.
A 45-minute call with a data modeling expert, tell us which of these you're solving for.
Trusted by data and AI teams at
Where Initiatives Stall
Three Places the Same Problem Shows Up
Your agents answer fluently and wrong
Ask a data question and an AI agent will answer confidently from whatever context it can find. Without a documented business model behind it, that context is often wrong.
Nobody can say what's actually in the warehouse
People change jobs, documentation goes stale, and reverse-engineering a legacy warehouse from scratch can take weeks before a migration even starts.
Every new data product starts from a blank page
Teams define the same entities differently across departments, so each new data product re-explains the model instead of reusing the last one.
One Model, Three Jobs
Wherever You Start, the Model Comes With You
Every model in Ellie carries AI-ready metadata, queryable through Ellie's MCP server, so an agent checks a governed definition instead of guessing from a schema.
Reverse-engineer Snowflake, Databricks, or Fabric into a documented model in one pass, methodology-agnostic, including Data Vault.
Conceptual, logical, and physical models stay linked through a shared glossary, so a new data product reuses entities instead of redefining them.
Three Ways In
Pick Where You're Starting
AI Context Agents Can Trust
Give any AI agent a governed definition to check before it answers, through Ellie's MCP server, a chat interface, or by turning existing text into a model in minutes.
- Chat with an AI assistant to draft a first model
- Query the business glossary directly from Cursor, Windsurf, or Claude Desktop via MCP
- Turn a user story or requirements doc into an editable model in under a minute
Reverse-Engineer What's Already There
Import your existing Snowflake, Databricks, or Fabric structure and get a documented model back, methodology-agnostic, Data Vault included.
- Reverse-engineer 170+ operational systems into a model
- Generate AI-assisted synthetic metadata for tables and columns you didn't document
- Support dimensional, data vault, mesh, lakehouse, and medallion approaches without forcing a rebuild
Design the Next One From the Last One
Gather business requirements with AI assistance, then carry the same entities through logical and physical modeling instead of starting over.
- Create a conceptual ER-diagram from plain text, a chatbot, or a diagram
- Link logical entities back to business glossary terms across model layers
- Generate a physical model and SQL presentation without losing the connection to the original entities
AI Context Agents Can Trust
Give any AI agent a governed definition to check before it answers, through Ellie's MCP server, a chat interface, or by turning existing text into a model in minutes.
- Chat with an AI assistant to draft a first model
- Query the business glossary directly from Cursor, Windsurf, or Claude Desktop via MCP
- Turn a user story or requirements doc into an editable model in under a minute
From People Solving These Problems
Ellie is a foundational platform. It helped us onboard hundreds of tables and thousands of columns from Microsoft Fabric while maintaining business context, and it helps AI agents understand how your data is structured and build better data products with a real understanding of what lives where.
Questions About Where to Start
No. Most teams start with one, often the warehouse migration already underway, and extend into AI context or new data product design once the first model exists.
A copilot can write a query. It can't tell you that "active customer" means something different in two systems. Ellie's glossary is what a copilot, or any agent, checks before it answers.
Alongside. Ellie is methodology-agnostic, it supports Data Vault, dimensional, mesh, lakehouse, and medallion approaches without forcing a rebuild of how your team already works.
They can draft the first conceptual model alone, through text, a chatbot, or a diagram. A data engineer still converts it to a physical model and pushes it to your pipeline.
A diagram tool stops at the picture. Ellie carries the same entities from conceptual through physical modeling, plus a business glossary and AI-ready metadata, in one canvas your whole team uses.
Bring Us Whichever One You're Solving For
AI context, a warehouse migration, or a new data product, start the conversation with whichever one is live for your team right now.
A 45-minute call with a data modeling expert, not a sales pitch.