Semantic context layer that helps AI agents understand your business data accurately
Source control for coding agents with conflicts settled before code exists
Stage
MVP
Startup
Founded
Not given
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Based in
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Pricing model
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Pricing
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Free: $0 for up to 3 projects and one person (never charges). Builder: $15 a month or $150 a year. Team: $36 per person a month or $360 per person a year. New accounts start on Team for 14 days free with up to 5 projects.
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
Agents declare plans before writing code
Written rules automatically settle conflicting plans
Integration with Claude Code, Codex, and MCP clients
GitHub repository mirroring after each merge
Intent-based workflow instead of pull requests
Drift detection comparing code changes to declared plans
No branch names; work identified by intent number
Write rules per folder with CI files people-only
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
Conflicts settled before any code is written
Agents review intent goals and plans, not line-by-line diffs
Works with your existing agents without leaving GitHub
Only humans approve merges, never agents or models
Kanlet
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