Make compressed air losses visible. Before they eat your budget.
Compressed air is expensive, but the biggest loss drivers often stay invisible. Upload pressure, runtime, and consumption data to spot patterns that point to leaks, poor control logic, or unnecessary base load faster.
- Production companies (SMEs)
- Mechanical engineers using compressed air
- Workshops & production halls
- Energy managers
The Problem
Compressed air is one of the most expensive energy forms on site, yet leakages, base load, and compressor behavior often remain unclear. Without data analysis, waste easily becomes normal.
- Loss drivers stay hidden across shifts and operating states
- Compressors run in poor combinations or too long at base load
- Consumption and actual demand are not compared cleanly
- Actions are prioritized from assumptions instead of data
The Solution
dAIve Predict turns compressed air data into a clearer action list: surface anomalies, compare patterns, and review the biggest levers first.
See when consumption and demand drift apart
Compare load profiles across time, shifts, and operating states to spot unnecessary base load earlier.
Find patterns that point to leaks
Use pressure, runtime, and consumption data to investigate suspicious phases more deliberately.
Review compressor strategy with data
See which combinations stay stable and where control logic or sequencing may be driving cost.
potential savings in compressed air systems when leak indicators, base load, and operating logic are reviewed with data instead of assumptions alone.
Indicative value from experience, to be verified with your data in a pilot.
How it works
Export data
Export pressure, runtime, and consumption from the control system.
Upload & analyze
dAIve Predict reveals patterns, drivers, and suspicious load phases.
Prioritize levers
See which operating states deserve review first.
Verify changes
Compare updated patterns and observe the effect.
Every hidden compressed air loss keeps running every day.
Start your 14-day trial and use your first dataset to see which losses you should tackle first.