Our mechanical systems are designed to support the full spectrum of AI workload densities, from traditional 10-15kW racks to extreme-density configurations exceeding 300kW per rack. For air-cooled infrastructure, we engineer high-efficiency CRAH units, hot/cold aisle containment, and optimized airflow management supporting densities up to 30kW per rack. For higher-density deployments, our hybrid air/liquid architectures combine traditional air cooling with rear-door heat exchangers or in-row liquid cooling units, enabling 50-100kW per rack.

For cutting-edge AI training clusters with Nvidia H100, H200, or B200 GPUs, we design complete direct-to-chip liquid cooling systems supporting 100-300kW per rack. These systems include facility chilled water loops, cooling distribution units (CDUs), rack-level manifolds, and cold plate integration optimized for specific GPU configurations. Our designs account for future density increases, allowing facilities to scale from air cooling to liquid cooling as workload requirements evolve without wholesale infrastructure replacement.

We've successfully deployed cooling systems across this entire density spectrum, from traditional enterprise datacenters to hyperscale AI training facilities. Our engineering team conducts comprehensive computational fluid dynamics (CFD) modeling and thermal analysis ensuring optimal performance for your specific hardware configuration and density requirements.

Timeline depends heavily on project complexity, site conditions, and delivery method, but typical ranges are 18-30 months from design initiation to full commissioning for a 50-100MW facility. Here's a breakdown:

Planning & Design (4-6 months): Conceptual design, utility coordination, permitting preparation, and detailed mechanical/electrical design packages. This phase includes equipment specifications, control narratives, and construction documentation.

Permitting & Procurement (3-6 months, parallel): Building permits, environmental approvals, utility interconnection agreements, and long-lead equipment procurement. Chillers, cooling towers, and major mechanical equipment often have 6-9 month lead times, making early procurement critical.

Construction (10-14 months): Site work, building shell, mechanical/electrical installation, and controls integration. Modular approaches using prefabricated equipment skids can reduce this phase by 30-40%, bringing timelines down to 7-10 months.

Commissioning & Startup (2-3 months): Functional performance testing, control sequence verification, integrated system testing, and operator training. This ensures all systems meet design specifications before tenant occupancy.

Phased approaches can accelerate time-to-revenue by bringing initial capacity online in 12-15 months while subsequent phases complete construction. Our prefabricated solutions and modular designs are specifically engineered to minimize deployment timelines while maintaining quality and performance standards. For existing facility retrofits or adaptive reuse projects, timelines may be shorter (12-18 months) since building shell and some infrastructure already exist.

Achieving world-class energy efficiency (PUE below 1.2) for AI datacenter infrastructure requires integrated optimization across multiple mechanical system components:

Free Cooling Integration: Our designs maximize hours operating in free cooling mode by leveraging favorable outdoor conditions. Water-side economizers using cooling towers or adiabatic dry coolers provide "free" cooling when outdoor wet bulb temperatures permit. In optimal climates, facilities can achieve 4,000-6,000+ annual free cooling hours, dramatically reducing mechanical cooling energy.

High-Efficiency Equipment: We specify premium efficiency chillers with magnetic bearing compressors, variable speed drives on all rotating equipment (chillers, pumps, cooling tower fans), and optimized heat exchangers minimizing pressure drops. These selections reduce mechanical system energy consumption by 20-30% versus standard efficiency equipment.

Advanced Control Sequences: Our building management systems continuously optimize equipment operation based on real-time loads, outdoor conditions, and utility rates. Chiller staging algorithms minimize total plant energy. Supply water temperature reset strategies reduce pumping and cooling tower energy. Demand-based controls adjust equipment speed and capacity to actual thermal loads rather than running at fixed speeds.

Liquid Cooling Benefits: Direct-to-chip liquid cooling enables higher supply water temperatures (up to 95-105°F versus 45-55°F for air cooling), dramatically improving chiller efficiency and expanding free cooling hours. Eliminating CRAH fans and reducing airflow requirements further reduces facility energy consumption.

Right-Sizing & Redundancy Strategy: Properly sized equipment operates at optimal efficiency points. N+1 redundancy provides adequate backup without grossly oversizing systems that run inefficiently at low loads. Our designs ensure equipment operates in peak efficiency ranges across expected load profiles.

Real-world results from our deployed facilities consistently achieve PUE of 1.10-1.18 for liquid-cooled AI infrastructure in temperate climates, with some facilities reaching PUE below 1.10 during optimal conditions.

Redundancy requirements vary based on workload criticality, financial impact of downtime, and budget constraints. Here's how we guide clients through this decision:

N+1 Redundancy (Most Common for AI Training): Central plant includes one additional chiller, pump, and cooling tower beyond what's required for full load. This configuration protects against single equipment failures while balancing capital cost and reliability. During maintenance events, one piece of equipment can be offline without impacting operations. This approach is standard for most AI training facilities where brief interruptions (15-30 minutes during failover) are acceptable. Capital premium: ~15-20% over base capacity.

N+2 Redundancy (Higher Reliability): Two pieces of backup equipment for each system type. Provides protection against multiple simultaneous failures or maintenance during equipment failure. Recommended for facilities with aggressive uptime SLAs or where workload interruptions cause significant financial impact. Capital premium: ~30-35% over base capacity.

2N Redundancy (Maximum Reliability): Completely independent, parallel mechanical systems each capable of 100% facility load. True fault-tolerant design where one entire system can fail or undergo maintenance without any impact on operations. Typically reserved for hyperscale inference infrastructure, financial services, or other workloads requiring five-nines (99.999%) uptime. Capital premium: ~90-100% over base capacity.

Distributed Redundancy: For multi-building campuses, we often recommend distributed plants where each building has dedicated mechanical systems. While individual buildings may be N+1, the campus benefits from geographic separation and isolation of failures.

AI Training Considerations: Unlike traditional datacenters, AI training workloads can often tolerate brief interruptions since training jobs checkpoint frequently. Many hyperscale operators find N+1 redundancy adequate, investing the capital savings into additional compute capacity. However, inference workloads serving production applications typically require N+2 or 2N designs due to real-time latency requirements.

We help clients model financial impact of downtime against redundancy costs, providing ROI analysis supporting optimal decisions for their specific requirements.

Yes, many existing datacenters can be retrofitted to support AI workload densities, though feasibility and cost-effectiveness depend on existing infrastructure capacity and building constraints. Here's our evaluation framework:

Central Plant Capacity Assessment: First, we evaluate whether existing chillers, cooling towers, and pumps have available capacity for increased thermal loads. Traditional datacenters designed for 5-10kW per rack density often have 50-70% of plant capacity available, allowing significant densification without central plant upgrades. If capacity is exhausted, we assess space for additional equipment and structural capacity for increased loads.

Facility Water Distribution: Existing chilled water piping, pumps, and expansion tanks must be evaluated for increased flow rates and pressure drops. Many facilities require upgraded pumps and larger diameter piping for liquid cooling distribution. We analyze hydraulic performance ensuring adequate flow delivery to all zones at target densities.

Rack-Level Infrastructure: Air-cooled facilities typically require complete replacement of CRAH units, addition of containment systems, and enhanced airflow management for 20-30kW densities. For liquid cooling, facilities need new CDU installation, rack manifold distribution, and quick-disconnect connections at each rack. We design phased approaches where high-density zones deploy liquid cooling while existing air-cooled zones remain operational.

Electrical Infrastructure: Increasing rack density often requires electrical upgrades including transformers, switchgear, UPS systems, and busway/whip capacity. We coordinate mechanical and electrical upgrades ensuring integrated design.

Building Constraints: Structural capacity for heavier equipment, ceiling height for equipment access and maintenance, and floor loading for increased rack weights all require evaluation. Most existing facilities can accommodate modifications, but extreme densities may be limited by building constraints.

Phased Approach: We typically recommend phased retrofits starting with pilot zones proving concepts before full-facility conversion. This minimizes operational disruption and allows iterative refinement.

Cost Considerations: Retrofit economics vary widely (typically $3-8M per MW of increased capacity) depending on existing infrastructure condition and density targets. We provide detailed feasibility studies and cost-benefit analyses helping clients decide between retrofit versus new construction.

Total installed costs for mechanical systems vary significantly based on cooling architecture, redundancy level, site location, and project delivery method. Here are typical ranges for budgeting purposes:

Air-Cooled Systems (10-20kW/rack):

• Central chilled water plant: $1,500-4,500 per kW of IT load
• Facility distribution and CRAH units: $800-2,200 per kW
• Controls and commissioning: $200-800 per kW
• Total: $2,500-7,300 per kW ($250M-730M for 100MW)

Hybrid Air/Liquid Systems (30-50kW/rack):

• Central plant with higher capacity: $1,800-3,800 per kW
• Facility distribution, CDUs, and rack-level: $1,200-3,100 per kW
• Enhanced controls and commissioning: $300-800 per kW
• Total: $3,300-7,700 per kW ($330M-770M for 100MW)

Direct-to-Chip Liquid Cooling (100-200kW/rack):

• Central plant optimized for liquid cooling: $1,200-3,500 per kW
• Complete liquid cooling infrastructure: $1,800-4,000 per kW
• Advanced controls and integration: $400-1,000 per kW
• Total: $3,400-8,500 per kW ($340M-850M for 100MW)

Cost Variables:

• Redundancy: N+1 adds ~15-20%, 2N nearly doubles costs
• Climate: Harsh climates require larger equipment and more sophisticated heat rejection
• Modular vs. Stick-Built: Prefabricated systems reduce field labor but have higher equipment costs
• Site Conditions: Difficult sites increase foundations, utilities, and access costs
• Location: Labor rates, permitting, and logistics vary 30-50% across regions

Important Notes: These figures include all mechanical equipment, installation labor, controls, commissioning, but exclude building shell, electrical infrastructure, IT equipment, land, and soft costs. Total project costs including these components typically run $8-15M per MW for complete AI datacenter facilities.

We provide detailed capital cost models during feasibility studies with line-item breakdowns, local cost adjustments, and escalation factors. For investors and developers, we also prepare investment-grade estimates meeting lender requirements with appropriate contingencies based on design completion level.

We recognize that family offices, sovereign wealth funds, and other capital-backed investors entering the AI datacenter market need more than just engineering—they need education, de-risking, and strategic guidance. Here's how we support first-time developers:

Feasibility Studies & Due Diligence: We begin with comprehensive feasibility analysis covering site evaluation (power availability, climate, connectivity), preliminary designs, capital cost modeling, operating expense projections, and market demand assessment. These studies provide investment committees with data supporting go/no-go decisions and establish baseline assumptions for financial modeling.

Investment-Grade Documentation: Our technical reports meet institutional lender requirements for project financing. We provide independent engineer validation of designs, cost estimates, schedules, and performance projections. This documentation is critical for securing favorable debt terms and construction financing.

Design-Build-Operate Partnerships: For clients preferring single-source accountability, we offer turnkey delivery managing design, procurement, construction, commissioning, and post-occupancy operations. This structure transfers risk from the developer to our team, providing budget certainty and schedule guarantees with performance bonds and comprehensive warranties.

Education & Strategic Counsel: We explain complex technical decisions in business terms, helping investors understand tradeoffs between capital cost, operating expenses, flexibility, and risk. We provide market intelligence on competitive positioning, technology trends, and tenant requirements. Our goal is empowering clients to make informed decisions rather than simply accepting our recommendations.

Vendor Management & Negotiation: Leveraging our industry relationships, we help clients qualify contractors, review proposals, negotiate competitive pricing, and structure contracts protecting owner interests. We provide checks and balances ensuring vendors deliver value while meeting quality standards.

Operational Transition: Beyond commissioning, we support clients through initial operations including staff hiring/training, preventive maintenance program setup, vendor contract negotiations, and performance optimization. We remain engaged until operations teams are self-sufficient or clients engage long-term support agreements.

Risk Mitigation: First-time developers face knowledge gaps that experienced operators instinctively avoid. We've seen (and prevented) costly mistakes including undersized electrical capacity, inadequate cooling for future densities, poor site selection, and insufficient redundancy. Our proactive risk identification and mitigation protects capital and reduces execution risk.

Financial Modeling Support: We provide technical inputs for pro forma models including CapEx assumptions, OpEx projections (energy, maintenance, staffing), capacity utilization curves, and technology refresh cycles. Our data helps underwriters, lenders, and investors validate financial projections.

Our "white-glove" approach has successfully guided dozens of first-time developers through their initial projects, with many becoming repeat clients for subsequent facilities.

While many engineering firms can design datacenter mechanical systems, we bring specialized capabilities particularly valuable for hyperscale AI infrastructure:

AI/HPC Specialization: We focus exclusively on high-performance computing and AI datacenter infrastructure. Our team understands GPU thermal profiles, training versus inference workload differences, liquid cooling integration with OCP/Nvidia platforms, and density roadmaps for next-generation accelerators. This specialization means we're solving the same challenges our clients face daily, incorporating lessons learned across our portfolio.

Hyperscale Experience: With 500+ MW of deployed AI infrastructure, we've engineered systems for the world's largest training facilities and inference platforms. We understand hyperscale requirements including standardized platforms for multi-site deployment, vendor qualification programs, supply chain resilience, and operational complexity at global scale. Our approved vendor status with tier-1 cloud providers reflects proven performance in the most demanding production environments.

Liquid Cooling Expertise: As AI densities exceed air cooling capabilities, liquid cooling expertise becomes critical. We've deployed direct-to-chip, rear-door heat exchanger, and immersion cooling systems across diverse configurations. We maintain technical partnerships with leading liquid cooling vendors ensuring our designs leverage best-in-class technology while remaining vendor-agnostic.

Speed to Deployment: Time-to-revenue is critical in competitive AI infrastructure markets. Our modular, prefabricated approaches reduce deployment timelines by 30-40% through factory testing, parallel construction activities, and streamlined commissioning. This speed advantage helps clients capture market opportunities and tenant commitments.

Financial Transparency & Value Engineering: We provide detailed cost breakdowns, competitive bid management, and proactive value engineering identifying savings without compromising performance. For investors and developers, we deliver investment-grade cost models, operational expense projections, and financial modeling support. Our goal is optimizing client ROI, not maximizing engineering fees.

Long-Term Partnership Approach: We view clients as long-term partners, not transactional projects. Post-commissioning support, technology refresh planning, expansion roadmaps, and performance optimization services ensure facilities continue delivering value across their lifecycle. Many clients return for subsequent projects based on our track record.

Global Capabilities with Local Expertise: Our service network spans North America, EMEA, and APAC with regional teams understanding local climate, codes, utility infrastructure, construction practices, and vendor ecosystems. This global reach with local expertise ensures consistent quality whether deploying in Oregon, Singapore, or Dublin.

Dual Audience Expertise: We serve both hyperscale operators requiring standardized platforms across dozens of sites AND first-time developers needing education and hand-holding through their first projects. This breadth means we can support clients from initial market entry through mature portfolio management.

Ultimately, we're judged on results: PUE performance, uptime reliability, schedule delivery, and cost management. Our track record across 500+ MW of AI infrastructure demonstrates consistent execution on these critical metrics.