The platform & energy model

Why months, not years.

Everyone is racing for power and land. We start with neither problem — the buildings, the footprints and the grid connections already exist. We turn real estate we control into deployable AI capacity.

Real estate we control

A tenant fit-out inside an existing building — change of use, not a ground-up build. No land assembly, no rezoning marathon.

Power-first siting

Every site is scored on substation proximity, voltage and grid headroom — power, not square footage, is the binding constraint we solve for.

Azure-native

Engineered with Microsoft so the platform runs the cloud-native stack operators already build for — distributed, but not bespoke.

Sovereign by design

Compute that stays in-jurisdiction — data residency, latency and compliance solved by geography, not by policy alone.
Time to first inference
The Edge
~Months
Greenfield colo
24–36 months
New hyperscale
36–60 months

Existing real estate + on-site power = activation, not construction.

Energy-intelligent

Compute and energy,
on the same site.

Co-located battery storage shortens time-to-power, trims demand charges and turns every node into a grid asset — built with Hanwha and Qcells.

  • Faster to power — storage bridges interconnection so nodes energize ahead of the grid.
  • Revenue-stacking — capacity, ancillary services and demand response in organized markets.
  • Resilient by default — uninterrupted inference through grid events, everywhere we operate.
Grid + on-site solar
Qcells generation, utility interconnection
Battery energy storage
Hanwha BESS — buffer, peak-shave, dispatch
Micro AI Factory
Dense GPU inference, Azure-native
Built for the workloads that can’t wait

Where milliseconds
are the product.

Inference at market speed

Agentic and real-time models that fall apart at 80 ms feel instant at 3 ms — close to the user, by design.

Sovereign & compliant

Healthcare, finance and public-sector AI that must stay inside a border — solved by where the compute sits.

Personalization at scale

Per-user, per-request intelligence served from the metro it lives in — without backhauling the world to one region.