How It Works
ClarityQ works in two phases: building context and asking questions.1
Connect Your Data Warehouse
Connect ClarityQ to your data warehouse — BigQuery, Snowflake, Redshift, PostgreSQL, Databricks, or MaxCompute. ClarityQ uses read-only access to query your data securely.
2
Build Your Context Layer
ClarityQ learns your business by building a Context Layer — a semantic model that maps your raw data to business concepts like metrics, features, segments, and dimensions. This is what makes ClarityQ’s answers accurate and relevant to your organization.
3
Ask Anything
Once context is built, anyone on your team can ask data questions in plain English. ClarityQ’s AI agent translates questions into SQL, executes queries, and returns answers with charts, tables, and insights.
4
Automate and Scale
Schedule recurring questions as automated tasks, share conversations with your team, and build up memory so ClarityQ gets smarter over time.
Key Capabilities
Ask Anything
Ask data questions in natural language. ClarityQ generates SQL, runs queries, and returns answers with rich visualizations — no SQL or data expertise required.
Context Layer
A searchable semantic layer that maps your raw data to business concepts. Includes table catalog, event catalog, semantic catalog, memory, and skills.
Automations
Schedule recurring questions as tasks and receive answers automatically via email or Slack on a cadence you define.
Memory
A two-tier memory system (personal and product) that retains context across conversations and improves answers over time.
Semantic Catalog
Define metrics, features, segments, and dimensions that reflect how your business thinks about data — not how your database stores it.
Skills
Encode your team’s best analysis methodologies into reusable skills. The AI agent follows them step by step — asking clarifying questions, executing the analysis, and validating results — so complex workflows run consistently every time.