- AI Product Teams: Prove systems safe for enterprise sales and customer trust.
- Regulated Industries: Healthcare, finance, insurance handling high-stakes AI workflows.
- Compliance Leaders: Security and risk teams ensuring regulatory alignment.
- Legal & Governance: Document AI behavior to mitigate liability exposure.
- Enterprise Evaluators: Buyers assessing vendor AI for safety benchmarks.
How to Use Superagent.sh?
- Initiate Analysis: Share AI systems for risk mapping with cross-functional input.
- Build Test Suites: Convert risks into real-world and edge-case focused tests.
- Execute & Review: Run suites to identify failures with structured evidence.
- Maintain Protection: Receive ongoing test suite updates against new threats.
- Managed End-to-End: Experts handle everything from risk ID to maintained suites.
- Catastrophic Focus: Targets legal/security harms, not just minor bugs.
- Custom Real-World Tests: Tailored to your data, systems, and failure modes.
- Audit-Ready Outputs: Structured reports for regulators and stakeholders.
- Lamb-Bench Transparency: Publishes LLM safety rankings publicly.
- Eliminates DIY testing burden with expert execution.
- Focuses on high-impact failures that matter most.
- Provides evolving protection beyond one-time checks.
- Transparent benchmarks build industry trust.
- Managed service less ideal for tiny experimental projects.
- Requires multi-team coordination for risk mapping.
- Pricing likely enterprise-focused, not startup-friendly.
- No instant self-serve dashboard option.
Custom
Pricing information is not directly provided.
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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