| What it does | AI agent orchestration platform that models workflow with triggers, actors, stages, gates, permissions, and outcomes
| Semantic context layer that helps AI agents understand your business data accurately
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| Key features | - Role-based organization with humans or AI agents in any position
- Workflow definitions with triggers, stages, gates, and outcomes
- Agent-agnostic design supporting Claude, Codex, Gemini, and local models
- Scoped credentials with least privilege access per stage
- Deterministic gates for tests, reviews, and approvals
- Audit trail with hash-chained tamper-evident ledger
- Local-first deployment with optional cloud hosting
- Isolated git worktrees for independent work and verification
- Budget tracking and spend monitoring per organization
- Separation of duties enforced in code
| - 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
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| What makes it different | - Orchestration layer that treats work as executable processes rather than remembered habits or wikis
- Roles swappable between humans and AI agents without rewriting workflows
- Credentials and secrets held by infrastructure, never exposed to AI models
- Autonomous AI decision-making within defined stages, deterministic controls at stage boundaries
| - 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
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