🤓 Latest Submissions
Enclave: Sovereign AI That Never Leaves
Enclave is a sovereign, on-premise multi-agent AI compliance system that runs entirely inside a single AMD Instinct MI300X node, no data ever crosses a network boundary. Banks doing loan underwriting, hospitals processing patient intake, and government agencies reviewing filings need agentic AI, but sending sensitive data to OpenAI or Anthropic's cloud APIs is often a compliance violation before the first token is generated. GDPR, HIPAA, and sovereign-AI mandates require data to stay in a controlled environment. Enclave solves this with a 4-agent pipeline; Orchestrator, Researcher, Analyst, and Auditor, coordinated entirely on one GPU. This is only possible because of the MI300X's large HBM capacity: fitting multi-agent context and shared KV-cache on a single card is the difference between staying fully on-prem versus needing multiple networked GPUs, which breaks the zero-egress guarantee regulators require. We validated this on real AMD Developer Cloud hardware (ROCm 7.2, vLLM 0.16): a full compliance review pipeline ran at 32.8 tok/s with a 14.35 GiB model footprint, and a concurrent 4-agent batched execution proved 1.9x throughput versus isolated cold calls, genuine evidence of single-node concurrent execution, not just an architectural claim. Our auto-tuner enforces the single-node constraint as a hard rule: any configuration requiring a second GPU is automatically rejected, regardless of raw speed, because a network hop between GPUs is a compliance boundary crossed. Enclave turns AMD's memory advantage into a literal compliance advantage, proving that regulated industries no longer have to choose between agentic AI productivity and data sovereignty.
13 Jul 2026
Autonomous Enterprise AI Auditor
Autonomous Enterprise AI Auditor is a multi-agent AI governance and compliance platform designed to help enterprises automatically audit, monitor, and improve their AI systems in real time. As organizations rapidly adopt generative AI and autonomous agents, enterprises face increasing challenges related to compliance, hallucinations, bias, transparency, security, and operational reliability. Our solution addresses these challenges by providing an intelligent auditing layer powered by advanced AI agents running on AMD GPU infrastructure. The platform uses multiple specialized AI agents working collaboratively to analyze enterprise AI systems from different perspectives. A Compliance Agent evaluates outputs against organizational policies and regulatory requirements. A Bias Detection Agent identifies unfair or potentially harmful responses. A Performance & Reliability Agent measures hallucination risks, consistency, latency, and output quality. A Risk Scoring Agent aggregates findings into a unified enterprise risk score with explainable insights and remediation recommendations. The system is powered by open-source Qwen models accelerated with AMD ROCm and AMD GPU infrastructure, enabling scalable and cost-efficient inference for enterprise workloads. Using AMD Developer Cloud and optimized inference pipelines, the platform delivers real-time auditing capabilities suitable for modern AI-powered applications, customer support systems, enterprise copilots, and internal AI agents. Our goal is to build a trustworthy AI auditing framework that helps enterprises confidently deploy AI systems while improving governance, transparency, and operational safety. By combining multi-agent orchestration, explainable AI analysis, and AMD-accelerated infrastructure, Autonomous Enterprise AI Auditor demonstrates how AI can be used to responsibly monitor and govern other AI systems at scale.
10 May 2026
