Velvt vs RELAI
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Velvt compared with RELAI
| Detail | V
Velvt | 
RELAI |
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| What it does | Assurance, research and network for autonomous AI agents
| The continual learning engine for AI agents
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| Stage | Startup
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| Founded | Not given
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| Based in | Not given
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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 | - Test agent behavior against explicit authority boundaries
- Preserve separate evidence of actor choice, authorization, containment and effect
- Bounded adversarial behavioral testing
- Repair and retest workflows with lineage
- Public Observatory for longitudinal research
- Provider-neutral and framework-compatible
- Customer-controlled assurance runtime
- Agent-to-agent collaboration and cross-agent encounters
- Test financial authority and spending limits
- Open integration surface with developer tooling
| - 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 | - Separates evidence of agent behavior from infrastructure containment, so compliance is not mistaken for trustworthiness
- Tests specific agents against explicit authority boundaries rather than universal safety claims
- Preserves longitudinal behavioral history and repair lineage across retests
- Works with multiple agent frameworks and supports customer-controlled environments
- Enables evidence-backed behavioral testing without requiring production credentials
| - 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
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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) | 1 ● no change since last check | 23 ● no change since last check |
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| Trust Flow (Majestic) | 4 | 13 |
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| Citation Flow (Majestic) | 24 | 25 |
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| Referring domains (Majestic) | 37 | 79 |
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3 details filled in for both companies.
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