AEGIS Initiative vs RELAI
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
AEGIS Initiative compared with RELAI
| Detail | 
AEGIS Initiative | 
RELAI |
|---|
| What it does | Governance-first security framework for autonomous AI systems
| The continual learning engine for AI agents
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| Stage | Startup
| Not given
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| Founded | 2026
| Not given
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| Based in | Not given
| Not given
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| Pricing model | Open source
| Freemium
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| Pricing | Not given
| RELAI Free: $0. RELAI Enterprise: Custom pricing.
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| Key features | - Constitutional governance charter with 11 articles defining AI autonomy boundaries
- ATX-1 threat matrix taxonomy for agentic AI systems
- Python runtime implementation of AGP-1 for policy evaluation
- TypeScript and Python SDKs for governance integration
- Federation protocol for cross-domain AI governance
- Versioned, citable, openly licensed specifications
- Standards engagement with NIST, IEEE, and NCCoE
| - 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 | - Governance-first approach where policy is evaluated before action, not after
- Deterministic enforcement with transparent audit trails and non-bypassable human oversight
- Non-partisan engagement with policymakers regardless of jurisdiction or political orientation
- Human-directed with transparent AI collaboration in research and development
- Deny by default principle: anything not explicitly authorized is prohibited
| - 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
| Not given
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| Platforms | Not given
| Not given
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| Public API | Not given
| Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 28 ▲ +14 (up) since last check | 22 ▼ −1 (down) since last check |
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| Trust Flow (Majestic) | 0 | 13 |
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| Citation Flow (Majestic) | 22 | 25 |
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| Referring domains (Majestic) | 6 | 79 |
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4 details filled in for both companies.
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