HyperNext BMS

Abstract

The traditional Building Management System was designed for environments where the workloads it served were unaware of the building infrastructure and largely indifferent to it. AI workloads have changed that. A rack-scale GPU system creates thermal and power dynamics that need sub-second visibility, predictive intervention, and supervisory control reaching across subsystems that used to run independently. This paper describes the HyperNext BMS, the supervisory platform that operates the Phase 1 Hyderabad campus and will scale to the 1.2 GW Kakinada AI Factory. It covers the operating philosophy (3-minute acknowledgement policy. Seven-layer security model. Role-based access). The technical implementation (Modbus, BACnet, SNMP, OPC-UA convergence into a single supervisory data plane). And operational discipline: alarm escalation matrix, tenant SLA handling, predictive maintenance triggers. The paper is intended for facility engineers, BMS integrators, and IT operations teams evaluating their own infrastructure for AI readiness.

Contents

1. 011. What a BMS is for, and what AI workloads change
2. 022. The 3-minute policy
3. 033. The seven-layer security model
4. 044. SLA handling
5. 055. Predictive maintenance
6. 066. Protocol architecture
7. 077. Alarm rule schema and the rule library
8. 088. Three incident case studies
9. 099. References and standards

Key findings

• The HyperNext BMS is a supervisory platform that reads underlying controllers and produces a single coherent operational view, with control actions gated by RBAC and dual-control where appropriate.
• The 3-minute acknowledgement policy with senior-approval for criticals enforces a discipline that scales beyond the cognitive capacity of any individual operator.
• The seven-layer physical security model with AI-camera reinforcement provides the audit-grade access control that Tier IV operations need.
• SLA handling runs as a formal workflow with seven steps, traceable from breach detection through prevention action, and audit-logged at every stage.
• Predictive maintenance based on continuous health-index computation reduces maintenance hours by 15 to 20 percent while reducing unplanned failures, complementing rather than replacing safety-critical scheduled tests.