Nirubi develops mathematically grounded structural resilience solutions for agentic AI workloads, mission‑critical software, and autonomous systems, ensuring they remain robust against failures and adversarial attacks. Their technology leverages formal verification and resilience engineering to provide provable safety guarantees for high‑stakes AI applications. The platform is built with support from the U.S. government and the Department of Defense, targeting enterprises that require trustworthy, fault‑tolerant AI deployments.
Funding
Funding not disclosed
Founders
Product
Problem
Advanced AI agents and autonomous systems are increasingly deployed in high‑stakes contexts such as defense, critical infrastructure, and mission‑critical software, where failures can cause severe safety, security, or operational consequences. Existing development tools lack mathematically provable guarantees of resilience, making it difficult to certify reliability under adversarial or unexpected conditions.
Solution
Nirubi delivers a platform that embeds mathematically grounded structural resilience into agentic AI workloads, mission‑critical applications, and autonomous systems. By applying formal methods and provable safety models, the platform enables developers to certify that AI behavior remains within defined safety bounds even under stress or attack. The solution integrates static verification, runtime monitoring, and automated fault‑containment mechanisms, providing a verifiable safety envelope for high‑risk deployments. Designed with support from U.S. government and defense agencies, Nirubi’s technology aligns with stringent security and compliance requirements, allowing organizations to deploy advanced AI with confidence in mission‑critical environments.
Target Audience
Primary customers are defense contractors, critical infrastructure operators, and enterprises developing mission‑critical software or autonomous systems that require provable safety and resilience for their AI components.
Features
- Formal verification engine that proves safety properties of AI decision‑making logic before deployment
- Real‑time runtime monitoring that detects and mitigates deviations from verified behavior
- Automated fault containment and graceful degradation to maintain system functionality under failure conditions
- mathematically defined resilience contracts that enforce provable robustness against adversarial inputs
- Integration pathways for existing AI frameworks and autonomous system stacks with minimal code changes
- Compliance‑ready reporting tools that generate audit‑grade evidence for defense and regulated sectors