For decades, industrial growth followed a predictable pattern.
Factories improved by:
• Adding machines
• Installing automation
• Hiring consultants
• Running isolated improvement projects
This worked — until complexity crossed a threshold.
Industrial systems didn't become complicated.
They became non-linear.
Hundreds of data streams
Dozens of vendors
Multiple stakeholders
Conflicting KPIs
Faster technology cycles
Shrinking margins
The result:
More data → weaker decisions
Yet the industry kept applying old models to new complexity.
Their business depends on selling tools — not eliminating unnecessary ones.
A true decision OS must reject 70–80% of potential solutions.
No vendor can afford that.
Traditional consulting is rewarded for:
• Longer engagements
• Larger decks
• Ongoing dependency
A decision OS compresses time and removes ambiguity.
That breaks the consulting incentive model.
Most platforms begin after decisions are already made:
• After sensors are chosen
• After pilots are approved
• After architectures are locked
The biggest leverage exists before technology enters the picture.
Three things had to happen simultaneously:
The industry needed proof that "tool-first" doesn't work.
Real insights had to come from the shop floor — not just boardrooms.
Someone had to see problems repeat across factories, industries, and use cases.
Only now do these conditions exist.
Because it didn't start as:
A product company
A consulting firm
A platform vendor
It started as:
A problem intelligence engine
Built by operators, integrators, and builders
who saw the same failures repeating — everywhere.
An operating system for decisions.
Not software.
Not services.
Not reports.
Infrastructure.