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. Synestra is designed to understand those relationships.
Architecture
Technical credibility without exposing implementation detail.
Synestra is an AI-native operating 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.
Synestra 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
Long-term learning
Snowflake, portfolio intelligence, customer-facing data services and long-term pattern discovery.
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, and NVLink health 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
Synestra understands what happens between systems.
Power ↔ Cooling
Electrical behavior can create thermal consequence.
Cooling ↔ Compute
Thermal constraint can reduce useful compute output.
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. Synestra 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
Synestra 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.