Agent accuracy
Your team asks questions. They get the right answers.
A semantic layer tells agents exactly what to query.
The reason most AI queries return wrong answers is the agent doesn't understand your schema. The semantic layer gives it measures, dimensions, and descriptions. So every answer is grounded in your actual data.
Capabilities
What the semantic layer gives you.
Measures, dimensions, descriptions. In plain English.
Five MCP tools on deploy. list_tables, describe_table, column_stats, search, query.
Self-correcting. Wrong table triggers helpful errors.
Agents query but never modify.
Raw data always preserved.
loony describe Tables: raw_stripe_charges 12,847 rows append raw_zendesk_tickets 3,291 rows append stg_revenue_support 892 rows replace Views: mv_account_health 892 rows Measures: total_revenue sum(amount) "Total revenue in USD" ticket_count count(id) "Number of open tickets" Dimensions: account string "Customer account name" month date "Calendar month" status string "healthy, watch, or alert"
Teams
How teams use it.
A PM asks “what's our churn rate this quarter?” and the semantic layer ensures the agent queries the correct defined measure.
Two analysts in different teams ask about revenue and get the same answer because it's defined once in the semantic layer.
Agents discover available tables, understand the schema, and write correct queries through five MCP tools that ship with every deploy.
tool: query input: "revenue by account, flag accounts where tickets > 10" Resolved: total_revenue (sum), ticket_count (count) Grouped: account Filter: ticket_count > 10 account revenue tickets status Initech $8,200 14 alert Globex Corp $4,100 12 alert Umbrella Inc $2,900 18 alert 3 rows · 8ms · read-only