Forge Orbital vs Perseus Computing
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Forge Orbital compared with Perseus Computing
| Detail | 
Forge Orbital | 
Perseus Computing |
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| What it does | AI accountability that grows into governance
| High-assurance infrastructure for AI agents
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| Stage | Not given
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| Founded | Not given
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| Based in | US
| US
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| Pricing model | Not given
| Open source
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| Pricing | Not given
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| Key features | - Governance: checks proposed AI actions against defined rules before execution
- Recorded decision records that can be reviewed, replayed and verified later
- Evaluation: compares AI agent performance using representative test cases and expected outcomes
- Optimization: recommends model and cache candidates based on usage, prices and evaluation evidence
- Regress detection identifies changed action paths and quality regressions
- On-premises and private cloud deployment
- Integration with existing AI tools via connectors and wrappers
- No model training on customer data; separate data boundaries from external AI providers
- Cost comparison and optimization across model and cache configurations
| - Harpe: chooses what an AI agent reads under a token budget with consistent results
- Kibisis: keeps agent memory on your hardware with AES-256-GCM encryption
- Argus: append-only record that detects edits with cryptographic verification
- No network code by default for local and disconnected use
- MIT License with published code, benchmarks and raw results
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| What makes it different | - Independent accountability: checks run inside your systems, not asking another AI to audit AI
- Your people keep authority: Forge produces decision evidence, your business enforces results
- Recorded decision evidence that auditors and regulators can examine
- Designed for consequential work in regulated industries like financial services
- Not another dashboard: integrates into your workflow without replacing your AI tools
| - Measured empirical validation across 6,111 questions outperforming full-context models
- Zero plaintext on disk with encrypted vaults matching plaintext accuracy
- 78.3% fewer tokens sent to the model with a 1,200-token budget per turn
- Preserves conversational chronology and relational qualifiers under token budgets
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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) | Not checked yet ● no change since last check | 42 ▲ +1 (up) since last check |
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| Trust Flow (Majestic) | 0 | 3 |
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| Citation Flow (Majestic) | 7 | 24 |
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| Referring domains (Majestic) | 22 | 62 |
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4 details filled in for both companies.
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