Neural AI OS - Framework for Development of Responsible AI Applications
Identifying potential Risks, assessing data biases, assuring transparency and explainability, mitigating risks with privacy by design, continuous monitoring and evaluation, and defined accountability structure and metrics
AI Assurance
Combining AI Guardrails and LLM Evaluation Towards Trustworthy, Explainable, Safe, and Ethical AI.
AI Engineering
Human- Machine Interface design principles, privacy by design to build Safe AI Systems with combining systems engineering principles, software engineering and computer science.
Cloud Architecture – AI and ML Perspective
Multi-Cloud Well-Architected Framework define principles and methods to design, build, and manage AI and ML Systems for operational, security, reliability, cost, and performance goals.