Free · directional · no contact details required
Most halls do not fail on cooling. They fail on a 63 A tap-off.
Thirteen constraints across electrical, thermal and physical domains decide how much AI compute an existing hall can carry. One of them binds first, and it is rarely the one people expect. Give the engine seven numbers you already know and it will tell you which — and which of your inputs nobody has actually measured.
Capacity qualifier
What actually stops your hall?
Seven numbers you already know. The same engine that produces our paid study returns the binding constraint, the deployable rack count, and the inputs nobody has measured yet. Capital cost and programme duration are part of the engagement, not of this read.
Want to see what the paid study looks like before you ask? Read one in full — the whole document, the deck, and the tables behind every number.
What you get
The binding constraint and why, deployable racks as the hall stands and after relief, the item that sets the date, and the list of inputs still assumed.
What you do not
Capital cost, programme duration in weeks, the full constraint ladder and the scenario comparison. Those are the paid engagement, and the engine withholds them server-side.
Why it is honest
Every figure carries an evidence class. A value you supply is customer data; a value we fall back to is an assumption, and it is named as one. A model applied to measured data stays a model.