Questions, answered.

Agami is the trust layer for your data. It sits between your AI tools (Claude, ChatGPT, Slack, Gemini, Cursor, custom agents) and your data warehouse, so every question your team asks gets the same governed answer. The semantic model is auto-generated from your schema, vetted by your data team, and gated by your auth and security rules before any query runs.

A trust layer is the system that decides what a business question means before it gets answered. It owns your definitions (what ARR means, what counts as an active account, how regions are grouped), your access rules (who can see what), and your auth (who is asking). Without a trust layer, an LLM invents answers from raw SQL. With one, every assistant returns the same governed answer because they all go through the same vetted definitions.

BI tools are for analysts building dashboards. Agami is for any team asking business questions from any tool. The semantic layer in a BI product is locked to that BI product’s UI; Agami’s trust layer serves the same definitions to Claude, Slack, Gmail, Notion, Cursor, custom agents, and any MCP client. You can keep your BI tool; Agami replaces or extends the semantic layer underneath.

A dbt semantic layer requires your team to author every metric, dimension, and join by hand and ship it through dbt Cloud. Agami auto-generates the first pass from your schema, your data team refines what matters, and the model stays in sync as the database evolves. Agami also integrates auth, row/column security, and surface integrations (Claude, Slack, MCP) that dbt’s semantic layer does not.

No. If your team has already invested in a semantic model in LookML, dbt MetricFlow, Cube, or another semantic-layer tool, Agami can read those definitions instead of generating new ones. You keep your existing source of truth; Agami’s trust layer adds auth, row/column security, and AI-client surface integration on top. You don’t end up maintaining two parallel definitions of ARR.

Agami queries in place against your existing warehouse or database. Supported sources include Snowflake, Databricks, Postgres, MySQL, Redshift, BigQuery, and Oracle. You can connect a new warehouse in hours using your existing read replicas, roles, and credentials.

No. Agami queries your warehouse in place using your existing credentials and read replicas. Nothing is copied, moved, or stored in Agami. The trust layer stores semantic definitions (what ARR means, what a region is) and audit logs; the actual rows live where they always did.

Agami auto-generates the first pass of the semantic model from your existing schema, including initial metric and dimension definitions. Your data team then reviews and approves every definition before it ships. The semantic model, not the LLM, picks the formula, grain, and joins for each question. As your schema evolves (columns dropped, tables added, new sources connected), the model adapts in place, and changes are reviewed the same way.

Agami exposes an MCP server (Model Context Protocol). Any assistant that speaks MCP (Claude, Claude Code, Cursor, custom agents) plugs in directly. For assistants without MCP support, Agami integrates through their tool/plugin APIs. The same governed answer comes back regardless of which assistant the question was asked in.

LiteBI is the open-source distribution of the Agami Analytics Agent. It is Apache 2.0 licensed and runs entirely on your machine: no Agami server, no API key, no telemetry. Install it in your IDE, write your definitions in YAML, deploy locally, and ask questions in plain English from Claude Code, Cursor, or any MCP client. The source is at github.com/AgamiAI/LiteBi.

LiteBI (the open-source local version) is free under the Apache 2.0 license. Teams and Enterprise tiers are quoted based on your team size and deployment requirements. Book a 30-minute conversation with the founders at /talk-to-us to discuss pricing for your scope.

The Enterprise tier is built for enterprise-level compliance: dedicated single-tenant deployment, detailed audit trails, priority support, and a setup engagement. Specific compliance certifications and customer security review workflows are covered in our security review and contract discussion. Email founders@agami.ai or book a call to talk through what your security team needs.

LiteBI runs locally in minutes once you clone the repo. Connecting a new warehouse on the hosted Teams tier takes hours, not months, because Agami uses your existing read replicas, roles, and credentials. A typical Teams deployment is live in days; Enterprise deployments include a setup engagement and run on your timeline.

Agami was founded by Sandeep Kachru (Co-founder and CEO) and Ashwin Ramachandran (Co-founder and CPTO). Sandeep built data warehouses, semantic-layer systems, and eval pipelines at scale. Ashwin led analytics and BI, turning the hardest business questions into trusted answers. Both ran data systems internally at large tech companies; Agami packages that pattern for everyone else.

Start a free trial at /free-trial. We will set you up with your own datasources and reach out within one business day to get you onboarded. If you would rather talk first, book a 30-minute conversation with the founders at /talk-to-us.