| What it does | Local-first orchestration layer that converts natural language goals into validated plans, policy checks, and real tool execution
| Semantic context layer that helps AI agents understand your business data accurately
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| Stage | Startup
| MVP
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| Founded | 2025
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| Based in | Not given
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| Pricing model | Not given
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| Pricing | Free: $0 with single local runtime, core tool registry, session memory, demo mode. Intelligence: $49/month with multi-agent delegation, Visual Workflow Studio, Security Center, MCP protocol, 7 LLM providers, native desktop app. Enterprise: custom pricing with distributed agent mesh, security policy
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| Key features | - Visual Workflow Studio with drag-and-drop pipeline canvas
- Natural language chain generation from plain English descriptions
- Step-by-step debugger with real-time node animation
- Policy gates and human approval for destructive operations
- Multi-agent delegation with consensus workflows
- AES-256-GCM encrypted secrets vault
- Support for 7 LLM providers including Ollama
- Immutable SOC 2-style audit log with SHA-256 hash chains
| - 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 | - Fully local execution with no cloud infrastructure dependency required
- Human-in-the-loop approval system for governance and risk mitigation
- Native desktop application with bundled runtime and zero prerequisites
- Visual pipeline design without code required
| - 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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| Free plan | Not given
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| Open source | Not given
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| Platforms | Not given
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| Public API | Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 26 ▲ +1 (up) since last check | 8 ● no change since last check |
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| Trust Flow (Majestic) | 1 | 1 |
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| Citation Flow (Majestic) | 23 | 24 |
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| Referring domains (Majestic) | 63 | 29 |
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MachinaOS
Kanlet