| Key features | - Connects to 15+ data systems including Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI and Postgres
- Extracts structural metadata including databases, schemas, tables, views, columns and relationship lineage
- Provides Web-UI, MCP server and REST APIs for accessing semantic context
- Automatic incremental updates with configurable sync schedules from on-demand to daily or weekly
- Role-level access controls and data classifications including PII, PHI and SPII
- Only reads metadata without accessing or moving actual data from systems
- Integrates with quality monitoring tools including Monte Carlo, Anomalo and Bigeye
| - One-click cloud deployment
- Support for multiple frameworks (OpenAI Agents SDK, Pydantic AI, LangGraph, CopilotKit)
- Built-in AG-UI endpoint for frontend integration
- Live session replay and debugging
- Streaming text, tool calls and approvals
- Durable sessions and message persistence
- Serverless human-in-the-loop approvals
- Local development environment matching production
- Scale to zero during waits
- Encrypted secrets management
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| What makes it different | - Eliminates confident AI mistakes by grounding agents in semantic context instead of having them guess from table names
- Reduces AI token costs and speeds up answers by resolving schema and joins once upstream instead of in each agent query
- Ensures consistent answers across all agents and teams using the same definitions and business logic
- Provides complete traceability and auditability of AI decisions with documented context and ownership
| - Works with any agent framework without framework-specific dependencies
- Handles infrastructure complexity like journaling, approvals, and state persistence automatically
- Stateless hosting with all conversation state managed on Sectr
- Native CopilotKit integration with no additional SDK or event parsing needed
- Approvals persist through browser refresh without losing agent progress
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Kanlet