ReachAILabsMake better decisions about the infrastructure AI needs
We help you plan and evaluate the data centers, GPU clusters and platforms behind serious AI work.
From power and cooling to cluster design and cost, we give you independent advice on the infrastructure decisions that are hardest to reverse.
Why it is different
AI changes how a data center is built
Facilities designed for web servers and databases struggle with GPU workloads. These are the four shifts that matter most.
Rack density is much higher
AI racks draw several times the power of a traditional enterprise rack. Many existing halls cannot supply or cool them without changes.
Cooling has changed
Air cooling reaches its limit at GPU densities. Direct-to-chip and other liquid approaches now belong in the early design conversation.
Power is the constraint
Grid capacity and equipment lead times, not floor space, set the schedule. Early power planning protects the whole project.
The network is part of the compute
Training jobs move data between GPUs constantly. A weak network wastes expensive hardware.
What we do
Advisory support across the full project
We advise on the decisions that set cost, speed and risk for your AI capacity. Builders and operators carry out the work.
Capacity and Workload Planning
We size compute, storage and network for the models you plan to train and serve, so you buy the capacity you need and not the capacity a vendor suggests.
Facility Design Advisory
Power density, cooling approach, floor loading and redundancy for GPU racks, reviewed against your site and your timeline.
GPU Cluster and Network Architecture
Cluster layout, interconnect and storage design for training and inference, including the trade-offs between cost, speed and reliability.
Site Selection and Power Strategy
Power availability, grid interconnection and lead times decide most AI data center projects. We help you test sites and options before you commit.
Cloud, Colocation and On-Prem Strategy
An honest comparison of renting, colocating and building, based on utilisation, data rules and total cost over several years.
Utilisation and Cost Review
We review how your GPUs are scheduled, cooled and monitored, and point out where utilisation and power or cloud spend can improve.
How we work
From first sizing to ongoing review
- 1
Assess
Workloads, growth, existing facilities, budget and constraints.
- 2
Design
Target architecture, power and cooling plan, build or buy options.
- 3
Select
Help choosing vendors and partners, reviewing proposals and checking the plan against your requirements.
- 4
Review
Periodic review of utilisation, cost and capacity as your demand grows.
Who we work with
Teams making their first or next big AI capacity decision
- Enterprises building private AI capacity
- Colocation and data center operators adding AI halls
- AI companies planning their first dedicated cluster
- Investors and developers assessing AI data center projects
Need the people to run it?
Infrastructure projects stall without the right engineers and technicians. Our AI Staffing practice recruits for AI platform, critical facilities and data center roles.
Planning AI capacity? Start with a conversation.
Tell us about your workloads, timeline and constraints. We will tell you what we would check first.