Forge Orbital vs RELAI
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 RELAI
| Detail | 
Forge Orbital | 
RELAI |
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| What it does | AI accountability that grows into governance
| The continual learning engine for AI agents
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| Stage | Not given
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| Founded | Not given
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| Based in | US
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| Pricing model | Not given
| Freemium
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| Pricing | Not given
| RELAI Free: $0. RELAI Enterprise: Custom pricing.
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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
| - Capture signals from code, requirements, and production traces
- Build and maintain EvalsWiki as versioned behavioral knowledge
- Find missing eval coverage and unclear expectations
- Generate complete evals with scenarios, environments, and evaluators
- Optimize harness across prompts, tools, workflows, and code
- Multi-objective optimization for quality, cost, latency, and KPIs
- Model selection, migration, and routing with evaluation
- Regression-controlled improvement with evaluation evidence
- Versioned storage in repository with pull requests
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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
| - Turns failures into replayable learning environments
- Prevents regressions by keeping previous evals in the loop
- Integrates with existing observability stacks and traces
- Preserves business intelligence versioned in repository
- Adapts harness to model changes without redefining use cases
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| Free plan | Not given
| 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 | 23 ● no change since last check |
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| Trust Flow (Majestic) | 0 | 13 |
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| Citation Flow (Majestic) | 7 | 25 |
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| Referring domains (Majestic) | 22 | 79 |
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3 details filled in for both companies.
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