Velvt vs Endgame
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 Endgame
| Detail | V
Velvt | 
Endgame |
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| What it does | Assurance, research and network for autonomous AI agents
| Context graph for AI-native revenue teams
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| Stage | Startup
| Established
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| Founded | Not given
| 2021
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| Based in | Not given
| Not given
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| Pricing model | Not given
| Contact for pricing
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| Pricing | Not given
| Not given
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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
| - One context layer connecting all GTM tools and AI agents
- Agent observability with end-to-end tracing
- Source citations for every answer
- Permission-aware answers scoped to user access levels
- Playbook grounding to enforce business semantics
- Corrections inbox for agent-generated changes
- Integration with Claude, ChatGPT, Slack, Salesforce and other tools
- Open Model Context Protocol (MCP) for any client
- SOC 2 Type II compliance and GDPR/CCPA support
- 140x faster answers via pre-compiled context
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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
| - 40x lower cost through pre-compiled context vs raw data processing
- 98% answer accuracy with no hallucinations through checked data
- Agent-agnostic: works with any AI tool your team uses
- Never trains on customer data with enterprise-grade security
- Set up within days with forward-deployed engineer support
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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) | 1 ● no change since last check | 48 ▼ −2 (down) since last check |
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| Trust Flow (Majestic) | 4 | 8 |
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| Citation Flow (Majestic) | 24 | 35 |
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| Referring domains (Majestic) | 37 | 323 |
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4 details filled in for both companies.
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