Aram Algorithm offers AI red teaming services to help organizations identify vulnerabilities and compliance gaps in high-risk AI systems, particularly for the EU AI Act. They provide phased engagements, from readiness assessments to evidence pack generation, enabling companies to mitigate risks and demonstrate regulatory due diligence.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations deploying high-risk AI systems face significant challenges in demonstrating compliance with emerging regulatory frameworks like the EU AI Act. Without structured testing and evidence generation, companies risk non-compliance, leading to market access restrictions and reputational damage.
Solution
Aram Algorithm provides specialized AI red teaming services designed to proactively identify vulnerabilities and compliance gaps in AI systems, particularly concerning the EU AI Act's requirements for high-risk applications. The company offers phased engagements, starting with initial readiness assessments and progressing to comprehensive evidence pack generation. This approach enables organizations to build a robust compliance posture, mitigate risks, and establish trust with regulators and stakeholders. Aram Algorithm's methodology focuses on stress-testing AI models against specific regulatory articles, producing actionable insights and documented proof of due diligence.
Target Audience
The primary target audience includes developers and providers of high-risk AI systems, startups seeking to demonstrate responsibility to investors, and corporate labs preparing for regulatory audits under frameworks like the EU AI Act.
Features
- AI red teaming engagements tailored to EU AI Act Articles 5, 50, 51–55.
- Generation of structured "Evidence Packs" and "Compliance Trap Reports" for regulatory submissions.
- Pilot programs for co-creating industry benchmarks and testing methodologies.
- Project-based audits for pre-launch AI system validation.
- Retainer-based continuous assurance services with quarterly adversarial testing.
- Development of documentation templates and gap-assessment frameworks for AI compliance.
- Focus on AI alignment and safety to ensure ethical and robust system performance.