From question to dashboard.
Ask in plain English — Datarelix runs a validated, read-only query and shows its work.
Ask in plain English
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Every answer comes from a query that actually ran — with the SQL on screen.
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Each statement passes a read-only validator; row limits are automatic.
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@ mentions pin tables into context; follow-up questions carry it forward.
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Failed queries revise and retry automatically, within strict limits.
validated · 10 rows
Connect what you already run
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On Azure SQL, Databricks, and Kusto, queries can run with each user’s own permissions.
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Parquet, Delta, CSV, JSON, and Excel files in cloud storage query like tables.
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Schema discovery maps your tables, with AI-written descriptions you can edit.
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Cross-source conversational analytics — one interface across every engine, each connection scoped to one database or dataset.
- PostgreSQL
- MySQL
- Azure SQL
- Databricks SQL
- Kusto
- BigQuery
- Amazon Athena
- Elasticsearch
- Snowflake
- Files & Object Storage
- Parquet
- Delta
- CSV
- JSON
- Excel
Chart it. Pin it. Refresh it.
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Line to heatmap — the chart fits the result, restyleable without code.
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“Add this to my Sales dashboard” pins the chart straight from the conversation.
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A refresh re-runs the stored query — one card or all of them.
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Results export as CSV; charts as PNG or SVG.
Revenue by customer
Bar · 10 rows
Revenue by month
Line · 12 rows
Guardrails you can check
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Read-only by design — no writes, no schema changes, no write path at all.
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Analysis code runs isolated, with no internet access.
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Credentials are encrypted and never enter the AI model.
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Every run is a step-by-step trace you can reopen.
Read the full security posture
Go deeper: how the read-only architecture is enforced and how credentials stay out of the AI model.
- Plan validated against a strict schema
- Query passed read-only validation
- Executed — 10 rows returned
tables · customers, orders
Platform
- Fully managed — nothing to operate
- SSO or email sign-in
- Documented REST API