These are the design decisions that are expensive or impossible to reverse once construction begins. Getting market-aligned input early is not a nice-to-have — it is risk mitigation.
| Design Decision | Getting It Wrong Means | Red&Teal Input |
|---|---|---|
| Power density per rack | Under-provisioned infrastructure that can't support AI loads — costly to upgrade post-build | Real AI tenant power requirements from our active demand pipeline |
| Cooling infrastructure | Air-cooled facilities that AI workloads overheat — liquid cooling retrofit is disruptive and expensive | Current-generation GPU cooling requirements by workload type |
| Power supply capacity | Insufficient utility capacity that caps your ability to attract high-density AI tenants | Power provisioning benchmarks based on live AI deployment data |
| Floor load and structure | Structural limitations that restrict the heaviest, most valuable GPU configurations | Floor load requirements for modern AI server configurations |
| Networking infrastructure | Standard Ethernet backbones that can't support the low-latency requirements of training clusters | Network specification guidance for InfiniBand and high-speed AI networking |
| Facility positioning | Marketing a facility as "AI-ready" without the specs to back it up — damaging credibility | Tenant profile modelling and competitive positioning guidance |
We translate real AI workload requirements — from our active demand pipeline — into infrastructure specifications. Your facility gets designed for actual tenants, not hypothetical ones.
Power provisioning levels, redundancy requirements, and utility engagement timelines — grounded in what AI tenants actually deploy, not theoretical maximums.
Rack density targets, floor layout considerations, and the infrastructure implications of different GPU configurations — from H100 clusters to full-scale B200 deployments.
Cooling requirements for AI workloads — from rear-door heat exchangers at moderate densities to direct liquid cooling and immersion at scale. We tell you what AI tenants are actually asking for.
Who your likely tenants are, what they need, and how to position your capacity in the market. We use our demand pipeline to tell you which AI companies and sectors are most likely to sign in your facility.
Real-time intelligence from the AI infrastructure market — demand trends, deployment patterns, emerging requirements, and competitor positioning — to inform both design and go-to-market decisions.
Red&Teal is not a structural engineering firm, MEP consultant, or data center architect. We provide the market-aligned demand intelligence that makes your technical partners' work more effective — and more commercially relevant.
The best time to engage us is before design decisions are finalised — when there is still time to incorporate real market demand into what you build.
Early-stage planning. Feasibility assessment. Before utility engagement. Before structural decisions are locked in. Before you commit to a cooling strategy. The earlier, the more valuable our input.
Your engineers know how to build it. We know what tenants actually need — and those two things are not always the same. We give your engineering team the market context that makes their decisions commercially sound.
We are an active intermediary in the AI infrastructure market — sourcing compute and generating demand for data centers every week. Our intelligence is live, not from last year's research reports. We know what AI companies are asking for right now.
General AI trends are — but the specific requirements of the companies likely to sign your leases are not. We tell you what our active buyers are actually specifying, not industry averages. There is a significant difference.
The earlier the better. Once structural decisions are made, the cost of incorporating market feedback rises sharply. Ideally, before utility engagement, before cooling strategy is locked in, and before density planning is finalised. Early input is cheap. Retrofit is not.