Managed Separately
Power • Cooling • Compute • Networking • Workloads
Behaves As One. Managed As Eight.
The central challenge of the AI factory.
Power generation, electrical distribution, cooling, networking, compute, workloads, operations and economics are typically managed independently, yet they increasingly behave as a single interconnected system.
Behaves As One System
Actions in one domain create consequences in another. Synestria is designed to understand those relationships.
Architecture
Technical credibility without exposing implementation detail.
Synestria is an operational intelligence layer that sits above existing infrastructure and operating systems, creating connected intelligence across relationships existing systems cannot see.
Architecture principle
Read from existing systems. Replace none of them.
Synestria sits above BMS, SCADA, DCIM, observability and workload systems to understand consequences across domains.
Gas Molecules To AI Tokens
The full operating chain matters.
Natural Gas
Power Generation
Electrical Distribution
Cooling
Networking
Compute
Workloads
Economics
AI Tokens
Three Layer Architecture
Local resilience. Campus correlation. Portfolio intelligence.
Edge Layer
Local intelligence
Kubernetes, TimescaleDB, OT integration and local telemetry processing at the building or zone level.
Campus Layer
High speed correlation
ClickHouse, relationship intelligence, operational memory and campus scale analytics.
Cloud Layer
Portfolio intelligence
Model-level learning across the operator network. Raw customer telemetry stays within your perimeter. Federated learning uses model aggregates, not operational data, to improve cross-site intelligence. Operators choose their participation level.
Why Three Layers Are Required
The telemetry volume exceeds human monitoring capacity at 100 racks. Campuses are building at 1,500.
Even a single building with 100 racks of AI infrastructure operating on 800VDC high voltage DC bus systems produces thousands of telemetry data points per second — per rack power draw, busway load, coolant inlet and outlet temperature, CDU health, GPU thermals, NVLink health, and fiber link error rates — across systems that were never designed to correlate with each other. A three layer architecture is not a design preference. It is the only structure that can process telemetry at rack level, correlate across domains at campus level, and build learning intelligence across time and portfolio. No single tier architecture handles the volume, latency requirements, and learning cycles that AI factory operations demand — at 100 racks or at 1,500.
Relationship Intelligence
Synestria understands what happens between systems.
Power ↔ Cooling
Electrical behavior can create thermal consequence.
Cooling ↔ Compute
Thermal constraint can reduce useful compute output.
Networking ↔ Compute
Physical fabric degradation creates silent compute throughput loss. A fiber link that reports healthy but drops traffic reduces GPU output without triggering an alarm across either domain.
Compute ↔ Workloads
Workload mix affects power, heat and economic output.
Workloads ↔ Economics
Scheduling decisions can influence revenue, cost and availability.
Operational Memory
Most infrastructure systems forget. Synestria is designed to remember.
Every telemetry event, commissioning issue, incident, root cause, resolution and operating pattern can become part of the accumulated intelligence layer.
Learning Engine
Observe. Correlate. Learn. Recommend. Assist. Automate.
ObserveIngest signals.
CorrelateConnect causes.
LearnBuild memory.
RecommendPrioritize action.
AssistHuman approval.
AutomateFuture state.
Human In The Loop
Synestria does not begin with autonomous control.
Recommendations remain subject to human review and approval. Future agentic operations are a roadmap direction, not a day one operational assumption.
Next step
See how Synestria integrates with your systems.
The integrations page details every system class Synestria reads — BMS, SCADA, DCIM, power management, and more — and what the connection requires.