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Use Cases

One platform, many kinds of facilities.

From hyperscale training clusters to retrofits of existing sites, AquaWatt DC OS adapts to how each facility runs. The scenarios below illustrate where the platform is designed to create value.

Hyperscale AI clusters

Challenge

GPU-dense halls push power and cooling to their limits, and small inefficiencies scale into large costs.

How AquaWatt helps

AquaWatt forecasts load, coordinates cooling and power together, and proposes optimizations that protect thermal headroom.

Target outcome

Initial pilot target: tighter thermal margins and lower energy per unit of compute.

Colocation & multi-tenant

Challenge

Operators must balance efficiency with per-tenant transparency and fair resource attribution.

How AquaWatt helps

Resource use is measured and attributed continuously, with sustainability reporting your tenants can trust.

Target outcome

Initial pilot target: clearer per-tenant resource visibility and reporting.

Enterprise & private AI

Challenge

Smaller AI facilities need enterprise-grade control without a large operations team.

How AquaWatt helps

A single command center and AI copilot help lean teams operate reliably and efficiently.

Target outcome

Initial pilot target: more headroom for small teams to manage complex facilities.

Retrofit existing facilities

Challenge

Legacy BMS, SCADA, and DCIM systems hold valuable data that is hard to unify and act on.

How AquaWatt helps

The AquaWatt Edge connects to existing protocols with no rip-and-replace, layering intelligence on top.

Target outcome

Initial pilot target: unified visibility across previously siloed systems.

Sustainability & ESG reporting

Challenge

Sustainability numbers are often modeled, delayed, and hard to defend to stakeholders.

How AquaWatt helps

AquaWatt ties every metric to measured outcomes, producing a defensible, auditable lineage.

Target outcome

Initial pilot target: verified, traceable energy, water, and carbon metrics.

Outcomes shown are initial pilot targets and illustrative platform demonstrations, not guaranteed results. Actual performance depends on each facility’s equipment, baseline, and operating conditions.

Which scenario looks like your facility?

Tell us about your site and we’ll map AquaWatt DC OS to your specific goals.

Who we serve

Built for the operators carrying the AI load

AquaWatt DC OS is designed for organisations whose electricity and water consumption is now a strategic constraint rather than a line item. These are the segments the platform is being built around.

Hyperscale cloud providers

Large multi-region estates where a fraction of a point of PUE compounds across sites.

AI infrastructure providers

Dense GPU clusters with volatile power draw and aggressive time-to-capacity pressure.

Colocation operators

Multi-tenant halls needing per-customer resource visibility and fair attribution.

Enterprise data-center operators

Owned facilities balancing uptime commitments against energy and water cost.

Telecommunications companies

Distributed edge and core sites where remote insight beats site visits.

Universities and research laboratories

Research computing with flexible batch schedules and constrained budgets.

Government computing facilities

Public-sector sites with reporting mandates and strict change-control processes.

High-performance-computing centers

Sustained peak utilisation where cooling strategy drives most of the resource bill.

Utilities serving data-center regions

Grid operators seeking visibility into large, fast-growing interconnection loads.

Developers and engineering firms

Teams designing new capacity who need evidence-based operating assumptions.

Initial ideal customer profile

Is your facility a strong first fit?

Early deployments go fastest where instrumentation, workload flexibility and organisational mandate already line up. The more of these that describe your site, the shorter the path from integration to verified savings.

Not every box needs a tick. Partial matches are still worth a conversation — the pilot readiness self-check on the pilot program page will show you exactly where the gaps are.

  • One or more facilities between 5 MW and 50 MW
  • High-density AI, GPU or HPC workloads
  • Metered power and cooling infrastructure
  • A BMS, DCIM or SCADA environment
  • Flexible batch or training workloads
  • Significant cooling cost
  • Water or grid-capacity concerns
  • A sustainability or energy-reduction mandate
  • A facilities team capable of supporting integrations
  • At least 90 days of historical operational data

Segments and profile criteria describe the customers AquaWatt DC OS is being designed for. They are not a statement of existing deployments or customer relationships.

Who it’s for

Every seat in the room sees a different problem.

Resource decisions in a data center are never made by one person. AquaWatt DC OS is designed around the seven roles that have to agree before anything changes on the floor.

Chief Infrastructure Officer

Objectives

  • Increase computing capacity.
  • Avoid infrastructure shortages.
  • Reduce operating cost.
  • Maintain portfolio-wide reliability.
  • Support corporate sustainability commitments.

What they need from the platform

Executive portfolio viewFinancial savingsRisk exposureCapacity forecastsInvestment recommendationsVerified resource-reduction results
Jobs to be done

What teams actually hire AquaWatt to do.

01

Determine where electricity and water are being consumed.

02

Predict resource demand before capacity constraints occur.

03

Find safe operational changes that reduce consumption.

04

Coordinate facility operations with computing workloads.

05

Shift flexible workloads to better times or locations.

06

Detect leaks, cooling inefficiencies and abnormal power use.

07

Validate resource savings after an action.

08

Demonstrate environmental performance to stakeholders.

09

Compare facilities and identify underperforming sites.

10

Automate repetitive optimization decisions.

11

Avoid decisions that reduce one resource while worsening another.

12

Prepare facilities for grid emergencies, droughts or heat waves.

13

Model future infrastructure and workload scenarios.