Mobb Hybrid-AI is an automated code remediation platform that combines generative AI with proprietary security research to fix code vulnerabilities. The platform analyzes code vulnerabilities and generates fixes by using a hybrid approach that integrates large language models with deterministic security research data to reduce hallucinations and improve accuracy. The system works by first analyzing code context before applying AI-suggested fixes. When additional context is needed, the platform requests the LLM to validate and expand on required context to ensure proposed solutions are viable. The platform uses proprietary data to enable the AI model to engage with the engine and reach consensus that combines LLM capabilities with security research. Mobb integrates into development workflows through GitHub and supports multiple programming languages. The platform is designed for application security teams, developers, CISOs, and DevSecOps teams to address security vulnerabilities, technical debt, and compliance requirements including SOC 2, PCI DSS 4.0, and executive order mandates. User data is not shared, used for training, or retained beyond portions flagged for vulnerabilities. All data is temporarily cached within the platform's secure environment. The platform provides fixes based on security best practices developed by security researchers, with AI handling precise and time-consuming tasks.