| What it does | Secure AI agents, with evidence
| Citations, built for AI agents
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| Stage | Not given
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| Founded | Not given
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| Based in | GR
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| Pricing model | Freemium
| Freemium
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| Pricing | Community: €0 (open source, 1x monthly testing volume, weekly monitoring, 3 seats, 30-day data retention). Team: €349/mo (20x monthly testing volume, 10 seats, 90-day data retention, webhooks, priority support). Enterprise: Custom pricing (contact for details).
| Free tier for small requests with no API key required; 10 million free tokens to start; $1 per million tokens for pay-as-you-go, with discounts available at volume or with annual commitments; failed requests are never charged.
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| Key features | - Automated adversarial testing with multi-turn and agentic attack chains
- Black-box testing without SDK installation or agent modification
- Four-tier Humanbound Firewall with LLM judge
- Continuous posture score from 0 to 100
- Framework mapping to EU AI Act, NIST AI RMF, OWASP LLM Top 10, OWASP Agentic AI Top 10
- Air-gapped testing with self-hosted models like Ollama
- Export compliance evidence in HTML, PDF, JSON, SARIF, CEF formats
- Webhook integration with SIEM and ticketing systems
- Apache-2.0 open source CLI, testing engine, and SDK
- Baseline campaign completion in hours
| - Token-level attribution with confidence scores
- Works with any AI model without access to weights or internals
- Processes documents up to 1 million tokens
- Returns results in under 2 seconds for 20,000-token documents
- On-premise and air-gapped deployment options
- PDF support with character-level mapping
- Supports 23+ languages including Indian languages
- Chrome browser extension for page and PDF tracing
- Raw token-by-token heatmaps available via API
- No data logging or training on request content
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| What makes it different | - Continuous monitoring instead of point-in-time reports
- Adaptive multi-turn attack chains that evolve over time
- Same open-source code as platform with nothing held back or limited
- No account required for local open-source testing
- Framework mapping and compliance evidence for regulatory requirements
| - Measures attention directly from the model's computation rather than asking the model to cite itself, providing verifiable attribution
- Achieves frontier-grade citation quality at 7x lower cost and 5-6x faster than alternatives
- Never confused by duplicate facts—identifies the exact occurrence the answer used even when values appear multiple times
- Lands on exact tokens down to character level, not just document chunks
- Works on any model's output post-generation without modifying inference pipeline
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 14 ▲ +1 (up) since last check | 2 ● no change since last check |
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| Trust Flow (Majestic) | 8 | 3 |
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| Citation Flow (Majestic) | 30 | 16 |
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| Referring domains (Majestic) | 71 | 36 |
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Humanbound
TokenPath