NoesisAI provides an agentic AI platform for AI training data centers that continuously monitors infrastructure, predicts failures, and autonomously executes remediation actions. The system optimizes energy use, speeds hardware onboarding, and offers real‑time dashboards and APIs for integration with existing DCIM tools, helping operators reduce downtime and operating costs.
Funding
Funding not disclosed
Founders
Product
Problem
Data centers that run AI training workloads often experience high energy consumption, frequent human‑error incidents, and slow onboarding of new hardware, leading to elevated operating costs and reduced reliability.
Solution
NoesisAI delivers an agentic AI platform that continuously monitors infrastructure, predicts operational issues, and autonomously executes corrective actions. By integrating predictive analytics with automated remediation, the system reduces energy waste, accelerates hardware provisioning, and minimizes downtime caused by manual errors. Real‑time dashboards and APIs expose actionable insights to operators, enabling smarter resource allocation and faster decision‑making without requiring extensive human intervention. The platform is designed to plug into existing data‑center management stacks, extending their capabilities with AI‑driven automation.
Target Audience
Primary customers are operators of large‑scale AI training data centers, AI infrastructure teams within cloud providers, and enterprise IT groups responsible for high‑performance compute environments.
Features
- Predictive failure detection using machine‑learning models that analyze telemetry from power, cooling, and compute subsystems.
- Autonomous remediation agents that execute predefined remediation scripts (e.g., load balancing, cooling adjustments) without operator input.
- Energy‑optimization engine that dynamically adjusts workload placement to minimize power draw while meeting performance SLAs.
- Automated onboarding workflow that provisions and validates new hardware, reducing setup time by up to 45 %.
- Real‑time monitoring dashboard with anomaly alerts, trend visualizations, and KPI tracking for reliability and efficiency.
- Open RESTful API and FHIR‑compatible connectors for seamless integration with existing DCIM and monitoring tools.
- Decision‑support analytics that recommend hardware upgrades or configuration changes based on cost‑benefit modeling.