A few trusted inputs produce business insights you can act on.
An AI-native industrial product for manufacturers, built and run on Harness. Starting from real machine actions, it connects dies, products, production tasks, costs and profits—showing managers what went wrong, how much it cost and who should handle it.
Owned and controlled by Big Cloud, independent of any model vendor. Models provide general intelligence; Harness organizes it into dependable industrial capabilities. Industrial data, calculation rules and business definitions remain in this layer when models change.
Business results depend on three agreed definitions: production cost, unit sales revenue and asset value. Confirming these determines whether the platform shows only machine status or the real cost and gross profit of every stroke.
Once definitions are confirmed, AimCloud organizes 102 metrics into seven modules, each addressing a business problem. Harness centrally maintains and validates their calculation rules and definitions.
How much work was done? Was capacity idle?
How long was it stopped? What did it cost?
Which electricity use was wasted?
How much has it been used? Is maintenance due?
What is the asset still worth?
What makes money? What loses money?
What is happening on the shop floor now?
AimCloud is built and run on Harness, which calls models for general intelligence. Models can be replaced anytime; industrial data, calculation rules and business definitions stay in Harness.
Turn production facts into verifiable results: a business dashboard, 102 metrics, AI diagnostics and closed-loop management tasks.
Industrial data, calculation rules and business definitions live in this layer. Valid strokes, actual output, downtime, costs and profits are calculated using verified rules and formulas—not model guesses.
Handles general comprehension, reasoning and recommendations. Switching models leaves the data, rules and definitions in the upper layers intact.
Start with one production line and two machines. Define the facts and business metrics first, then see what the software can deliver.
