Why we built Gaamma
Most factory floors run on a patchwork: one system for work orders, another for inventory, a third for quality, and equipment data that never reaches any of them. Nobody gets a full picture until something has already gone wrong. We built Gaamma to unify that data and, more importantly, to predict from it instead of just reporting on it after the fact.
The problem
Work orders, machines, and operators lived in separate systems, so no one could see exactly where a job stood. Equipment failures showed up as unplanned stops instead of scheduled maintenance, and manual reordering kept inventory turns flat.
What disconnected systems cost manufacturers:
- • No single view of where a work order actually stands across the floor
- • Equipment failures show up as unplanned stops instead of scheduled maintenance
- • Manual reordering keeps inventory turns flat instead of optimized
- • Integrating with existing PLCs and SCADA systems is treated as a rip-and-replace project
How we built it
We built Gaamma as a single production layer that both ingests floor data and predicts from it. Equipment monitoring feeds a machine-learning model that flags failures before they happen, while the same platform standardizes work order, inventory, and quality data on one source of truth—connecting to PLCs, SCADA systems, and IoT sensors already on the floor instead of asking manufacturers to replace them.
One source of truth across the floor
Work orders, machines, and operators are tracked in real time in a single system, instead of three systems that each know part of the story.
Predictive maintenance from live equipment data
A machine-learning model trained on equipment monitoring signals flags likely failures early enough to schedule maintenance instead of reacting to a stopped line.
No rip-and-replace integration
Gaamma connects to PLCs, SCADA systems, IoT sensors, and accounting software already on the floor through standard protocols, so existing infrastructure stays in place.
Built for how different factories actually run
Discrete manufacturing, process/batch production, job shop, and warehouse operations each get the workflows they actually need, on the same platform.
What shipped
Real-time work order, machine & operator visibility
Predictive maintenance model flags failures before they happen
Connects to PLCs, SCADA & IoT sensors without rip-and-replace
Serves discrete, process, job-shop & warehouse operations
Key engineering decisions
Unify the data before automating anything: Predictive maintenance is only as good as the data feeding it—consolidating work order, inventory, and equipment data came first.
Predict, don't just report: A dashboard that shows what already broke is less useful than a model that flags what's about to.
Integrate with what's already running: Standard protocol support for PLCs and SCADA systems meant manufacturers didn't have to replace hardware to adopt Gaamma.