Why Agami
The trust layer, not another destination.
Ask in Claude, ChatGPT, Gemini, or Copilot, and Agami answers across your databases through one shared semantic model, with your security and governance enforced.
Compare approaches
Four ways to put AI on your data. One is built for trust.
Most teams reach for one of these. Here is how the approaches differ.
Works with any assistant
Use Claude, ChatGPT, Gemini, Copilot
- Agami Any assistant
- Raw MCP / DIY You build and maintain each connection
- Warehouse-native AI Via their MCP, scoped to that platform
- Single-vendor BI AI Strongest inside the vendor's own app
Answer arrives in the tool you already work in
- Agami
- Raw MCP / DIY
- Warehouse-native AI
- Single-vendor BI AI Mostly the vendor's UI
Connects all your data
Span multiple databases in one answer
- Agami Across sources
- Raw MCP / DIY You orchestrate it
- Warehouse-native AI Data must live in that warehouse
- Single-vendor BI AI Depends on connectors
No need to move or centralize your data
- Agami
- Raw MCP / DIY
- Warehouse-native AI Assumes data in Snowflake / Databricks
- Single-vendor BI AI
Trusted answers (shared semantic model)
Shared definitions, so the same question gives the same answer
- Agami Governed semantic model
- Raw MCP / DIY Raw schema, no shared meaning
- Warehouse-native AI Within their model
- Single-vendor BI AI Within their model
Consistent across every assistant and user
- Agami
- Raw MCP / DIY
- Warehouse-native AI
- Single-vendor BI AI
Security and audit
Central access control and full query audit
- Agami
- Raw MCP / DIY You assemble it
- Warehouse-native AI Inside that platform
- Single-vendor BI AI Inside that platform
One policy layer across all sources and assistants
- Agami
- Raw MCP / DIY
- Warehouse-native AI Per platform
- Single-vendor BI AI Per tool
Performance
Tuned for fast, reliable query execution
- Agami
- Raw MCP / DIY Your responsibility
- Warehouse-native AI
- Single-vendor BI AI
Improves with use
Context sharpens as your team asks more
- Agami
- Raw MCP / DIY
- Warehouse-native AI
- Single-vendor BI AI
What only Agami does
Neutral on purpose
Cortex needs your data in Snowflake. Genie needs it in Databricks. A BI tool needs you inside its app. Agami connects the assistant your team already chose to the sources you already have, governed in one place.
Trust is more than context
A semantic model is necessary but not sufficient. Agami adds security, performance, audit, and governance around the context, so an answer is not just plausible, it is one you can stand behind.
It gets better the more you use it
Agami's context sharpens as your team asks real questions, so answers improve over time instead of drifting.
When you might not need Agami
If all of your data already lives in one warehouse, your team is happy working inside that vendor’s tools, and you do not expect to use more than one AI assistant, a warehouse-native or BI-native option may be all you need. Agami earns its place when your data spans more than one system, when people want answers inside the assistant they already use, and when you need one governance and audit layer across all of it.
Works with the assistants your team already uses
Gartner projects that by 2028, 60% of agentic-analytics projects relying solely on MCP will fail without a consistent semantic layer.
Questions buyers ask
Isn’t MCP enough on its own?
MCP is the plumbing that lets an assistant call your data. It does not give you shared definitions, security, audit, or consistent answers across sources. That semantic and governance layer is what Agami adds on top, and it is what keeps answers trustworthy at scale.
Do I need to move my data?
No. Agami connects to your existing databases where they are. Your data does not have to be centralized in one warehouse.
Does it work with ChatGPT and Claude, or just one?
Any of them. Agami is assistant-agnostic and works with Claude, ChatGPT, Gemini, and Copilot, so you are not locked to one vendor’s assistant.
How is this different from a semantic layer like Cube or dbt?
Those define metrics. Agami uses a semantic model and also serves governed answers across sources to any assistant, with security, audit, and performance built in. Context is necessary but not sufficient on its own.
How is it different from a BI tool’s AI?
BI-tool AI is strongest inside that tool’s own app. Agami delivers the answer inside the assistant your team already uses, across sources the BI tool may not own.
Is it secure enough for enterprise data?
Agami enforces central access control and keeps a full audit of every query, across all connected sources and assistants.
See it answer a real question against real data.
Try the demo, or book a 30-minute conversation with the founders.