Data center with advanced cooling systems
AI-Powered Cooling Optimization

Intelligent Cooling That Unlocks AI Capacity

Cut cooling energy by up to 40% and redeploy the power you save to compute. Recover stranded capacity, defer chiller and CDU purchases, and plan expansions with data instead of worst-case guesses.

Backed by Techstars

The Cooling Efficiency Gap

1.56
Average PUE
Industry standard
1.03
Optimal PUE
Potential with our optimization
$5.8B
Wasted Annually
From cooling inefficiency
40%
Potential Savings
AI-driven reduction

Every 0.1 improvement in PUE translates to millions in annual savings for enterprise data centers.

The Challenge

Data Centers Are Energy Hungry

3%

Global Electricity

Data centers consume 3% of worldwide electricity, projected to reach 8% by 2030 as AI and cloud computing accelerate.

40%

Cooling Energy

Cooling systems alone account for 40% of total data center energy consumption, representing billions in annual operating costs.

8%

By 2030

Traditional static cooling approaches waste enormous energy by over-provisioning, unable to adapt to dynamic thermal loads.

Our Solution

AI-Powered Dynamic Cooling

A four-stage intelligent system that continuously optimizes your data center cooling in real-time.

01

Real-Time Monitoring

Continuous sensor data collection across temperature, humidity, workload, and power metrics.

02

Predictive Analytics

Deep reinforcement learning forecasts thermal loads based on workload patterns and environmental conditions.

03

Dynamic Optimization

Automated adjustment of fan speeds, chilled water flow rates, and liquid cooling loops in real-time.

04

Continuous Learning

System adapts and improves efficiency through ongoing training on operational data.

Advanced liquid cooling system in a modern data center
AI neural network visualization for thermal optimization
Deep Reinforcement Learning

How It Works

Our proprietary DRL agent continuously learns optimal cooling policies by analyzing multiple data dimensions simultaneously.

Observing multi-dimensional state space: server temperatures, ambient conditions, workload metrics, and energy consumption

Predicting thermal load trajectories 15-30 minutes ahead using LSTM neural networks

Optimizing cooling actions to minimize energy while maintaining thermal constraints

Adapting to seasonal variations, workload shifts, and equipment degradation

35-40% more energy efficient than traditional PID controllers and rule-based systems.

CapEx & Capacity Planning

Turn Cooling Overhead Into Compute Capacity

Most AI data centers aren't limited by floor space. They're limited by power. Every megawatt spent on over-provisioned cooling is a megawatt that can't run GPUs. Synapse Thermal recovers stranded cooling capacity and gives your team the forecasts it needs to plan expansions with confidence, not guesswork.

01

Recover Stranded Capacity

Static setpoints and worst-case provisioning leave cooling headroom locked up. Our DRL agent matches cooling to real thermal load, freeing capacity you've already paid for.

02

Defer Cooling CapEx

Before you buy another chiller, CRAH, or CDU, find out how much more load your existing plant can safely carry. Optimization often pushes major cooling purchases further out.

03

Shift Power From Cooling to Compute

At a fixed utility interconnect, lower cooling energy means more power available for IT load. Improving PUE directly expands how much compute your site can host.

04

Forecast-Driven Planning

Our models forecast thermal load 15-30 minutes ahead and learn your site's behavior over time. That gives facilities and finance teams data-backed headroom estimates for rack additions, density upgrades, and phased build-outs.

Every 0.1 improvement in PUE frees power that can be redeployed to revenue-generating compute.

Get a Capacity Assessment
800 VDC Transition

De-Risk Your Move to 800 Volt Power

Next-generation AI racks are moving to 800 VDC power architectures to support rack densities approaching 1 MW. That transition rarely happens all at once. Facilities run mixed AC, 48V, sidecar, and 800 VDC architectures simultaneously.

Market Opportunity

Perfect Storm of Drivers

$20B addressable market by 2028, growing at 23% CAGR

AI Explosion

GPU workloads generate 3-5x more heat, demanding intelligent cooling

Sustainability Mandates

Corporate carbon neutrality commitments and regulatory pressure

Energy Costs Rising

Electricity prices up 25% YoY, making efficiency critical to margins

Market Size by Segment ($M)

Enterprise DCHyperscaleColocation$0B$2.5B$5B$7.5B$10B
  • 2024
  • 2028
Why Synapse Thermal

Competitive Advantages

01

Proprietary DRL Algorithm

Thermal prediction and multi-objective optimization. Simulated 15-20% better than competing solutions.

02

Hardware-Agnostic Integration

Works with existing HVAC, CRAC, liquid cooling, and immersion systems. No rip-and-replace — rapid deployment in 4-6 weeks.

03

Edge AI Deployment

On-premise inference for microsecond latency and data privacy. No cloud dependency for mission-critical control systems.

04

Deep Industry Relationships

EPC and Big Tech relationships. Strong references and case studies from enterprise deployments.

Leadership

The Team

Industry veterans with deep expertise in data center operations, machine learning, and enterprise technology.

Thomas Chepucavage

Thomas Chepucavage

CEO

Jimmy Chui

Jimmy Chui

CTO

Prof. Abdullah Mueen

Prof. Abdullah Mueen

Advisor

Nick Pascetti

Nick Pascetti

Solutions Architect

Get Started

Ready to Optimize Your Cooling?

Customers achieve full investment return in 8-14 months through energy savings alone. See how Synapse Thermal can transform your data center efficiency.

Contact Tom Chepucavage to schedule a demo

Synapse Thermal

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