Orgabot vs Skan AI
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Orgabot compared with Skan AI
| Detail | 
Orgabot | 
Skan AI |
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| What it does | AI agent orchestration platform that models workflow with triggers, actors, stages, gates, permissions, and outcomes
| Not given
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| Stage | Not given
| Established
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| Founded | Not given
| 2020
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| Based in | US
| US
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| Pricing model | Not given
| Contact for pricing
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| Pricing | Not given
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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
| - Context Graph of Work - continuously updated model of how work gets done across enterprises
- Blueprint for AI roadmap planning with defensible ROI recommendations
- Process Intelligence to identify bottlenecks and prioritize automation
- Engineering Intelligence for unified view of developer work
- Work-aware agents grounded in real operational context
- Zero-integration capture across legacy and modern systems
- In-environment processing with data staying in customer perimeter
- Agentic Operating Procedures for governed agent execution
- Audit logs and compliance tracking for every agent decision
- Built on 25B+ events from 500+ observed processes
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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
| - Observes actual work at scale across enterprise systems rather than relying on assumptions or documentation
- Delivers AI agents that work in production at scale, not just pilots
- Provides measurable ROI and audit trails for AI spending and decisions
- Privacy-first architecture keeps sensitive data within customer environments
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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) | 24 ▲ +1 (up) since last check | 54 ● no change since last check |
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| Trust Flow (Majestic) | 7 | 14 |
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| Citation Flow (Majestic) | 31 | 38 |
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| Referring domains (Majestic) | 18 | 463 |
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3 details filled in for both companies.
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