NexOS Intelligence
Multidirectional AI Automation
Solving Tomorrow, Today
The console
One engine reading four altitudes of the same operation, national picture, branch drill-down, supply continuum, expansion, with a human gate on every consequential move.
Fictional operator · synthetic data · replay, a capability demonstration, not client data
Multidirectional AI Automation
A hospital, a grid, a compute fleet and a kitchen fail the same way: demand crosses the capacity actually available, and the warning was already showing in a channel nobody had connected to the one that broke.
Our wins
Four arenas, each calibrated in its own domain. Every figure is from a preregistered test, fit on train and scored on an untouched test set, with the pass condition written before the data was opened.
Hospital admissions
+10.6–19.9%
floor skill over the baseline it is forbidden to lose to
Brier 0.041–0.047 vs 0.067
NHS · 137 trusts · daily
Power grid
+30.4–61.0%
floor skill over persistence at the measured lag
Brier 0.005–0.030 vs 0.033
EIA-930 · 37 balancing authorities · hourly
Grid, five-minute
+9.5–19.7%
held all the way down to a five-minute grain
Brier 0.005–0.014 vs 0.046
NYISO · 11 zones · 1.77M ticks
Production compute
+12.0–21.5%
on a real production trace, in the arena we predicted would fit
Brier 0.031–0.035 vs 0.049
Azure Functions · 300-function cohort · 5-min
Radar
Read the system against its own limit.
One product with two ways in. Start at the read, or take the whole instrument. Tested across four arenas against gates written before the data was opened.
Forge
The Forecast + Risk Engine
Forge is what a trader betting on energy or AI-infrastructure strain actually wants: the number, not the loop. Telemetry through Echo: the read, the strain state, and the calibrated call. Sellable on its own.
Forge reads the Telemetry. Echoes are called when breach risk is found on the horizon.
Radar
The full instrument
Forge, and then what happens next: the corrective move, the authority it routes to, the record of whether the call was right, and the loop that closes on failures that keep recurring. Radar runs on Forge.
The system flags. The operator decides. It never executes alone.
The engine is re-fit to each operation it reads. The models stay in their domain. The discipline travels.
Studio
Some problems do not need an instrument.
They need somebody who has run the floor to look at yours and say what is actually wrong. Studio is the agency door. Two ways in, both operator-to-operator, neither of them a platform subscription.
Diagnostic
$997
A focused, operator-to-operator read of where an operation is leaking time, labour, and margin. Delivered as a written read you keep, inside seventy-two hours. It converts naturally into a retainer, and it is the fastest way to find out whether we are useful to you.
Custom Projects
Priced after scope
Scoped to the problem rather than to a package. Discovery through to a clean handover, and you own what is yours at the end of it. This is the wedge: the method re-fit to one operation, which is the part of the thesis that survived the kill test.
Tell us what you run.
NexOS Gauge · the free read
Fifteen questions about the automations you run and how you watch them. You get a graded read with the three fixes that matter first. It is not a demo script.
Academy & Readout
The discipline, in public.
One of these teaches you to read your own operation the way the engine reads it. The other prints our calls before the outcome is known, then grades them where anyone can check. A company that says its edge is honesty has to be checkable from outside.
Academy
Five modules · $247
A course for operators, not engineers. It starts at the foundations of machine intelligence, hands you the Agentic Table, and ends with you graphing your own operation: your data through a workflow you composed, with the human gate holding the last word. The first three modules are free. You reach the paywall only after you have seen whether it is any good.
Readout
Issue No. 1 · free to read
We run the news through the same discipline we point at grids and compute fleets: state the call, state the probability, name the kill condition before the data lands, and print the failures beside the wins. The models are fit to their domains and stay there. The discipline travels.