Edge-native intelligence
Processes infrastructure data close to where it's produced, enabling faster awareness of changing facility conditions.
Edge-Native Bio-Inspired Agentic AI for Data Center Capacity Orchestration
Unlocking stranded AI capacity - without building more infrastructure.
Supported by:
Draper Ventures
Harvard
Innovation Labs
The capacity is there. The orchestration isn’t.
- Power, cooling, and compute capacity sits out of reach of AI workloads.
- The provisional peak problem: Facilities, cooling, and power systems are all sized for rare peak-day loads—not everyday demand. That safety margin sits idle by design, on every layer, almost all the time.
- Static limits and disconnected systems hide usable headroom.
- PhysaFlow reveals stranded capacity and its constraints.
- Operators can assess more AI demand with the same existing infrastructure.
Stranded capacity
Turn siloed infrastructure into one orchestrated AI system.
Explore the PlatformOne shared operating picture
Connects facility conditions, IT capacity, and workload demand across systems that traditionally operate independently.
Observe first. Validate the opportunity.
Automate when you’re ready.
Edge-Native Bio-Inspired Agentic AI
Uses distributed intelligence to identify dependencies, evaluate tradeoffs, and safely reallocate power, cooling, compute, and workloads as conditions change.
Sustainable Belongs Inside Decisions
Sustainability-aligned framework that informs operating policies; every co-benefit requires its own measurement boundary and evidence.
Discover the value of your facility's stranded capacity.
Adjust your facility’s capacity to see the modeled stranded capacity and annual opportunity cost, assuming 35% is stranded.