Optimize throughput with no-code Industrial AI.
Most lines run below their actual capacity because setpoints stay conservative. Intelecy finds the bottleneck of the moment and tells operators how far they can safely push.
What makes this operationally expensive today.
Hidden bottlenecks
The constraint shifts between units and shifts; static dashboards miss it.
Conservative setpoints
Operators leave headroom on every loop because nobody owns the trade-off.
Lost capacity
Small slowdowns add up to whole shifts of missed production each month.
The process indicators behind every recommendation.
Intelecy reads these signals from your Historian, DCS, and SCADA — no new sensors required to get started.
From signal to action, mapped to the modules.
- STEP 01Data Explorer →
Identify the live bottleneck
Cross-tag analysis surfaces the unit that is actually capping the line right now.
- STEP 02Forecasting →
Forecast capacity ahead
Project throughput under current setpoints so supervisors plan the shift, not react to it.
- STEP 03Prescriptive optimization →
Recommend the next move
Setpoint guidance with expected tonnes/hour and the quality envelope to respect.
- STEP 04Closed-loop automation →
Promote winning loops
When a recommendation is consistently safe and right, promote it to governed closed-loop.
How customers measure success.
Metals producer, Northern Europe
"Operators get a number and a why. They know exactly how much further they can push the rolling mill this shift."
What it takes to stand this up.
Throughput counters and constraint-unit tags. Quality and energy tags to bound the recommendations.
Bottleneck mapping in week 1; first prescriptive model in 2–3 weeks.
Operations director, process engineer, shift supervisors, planning.
Historian + MES counters via the Gateway. Recommendations in the workspace or surfaced in the HMI.
See it on your own plant data.
A 30-minute working session with our process AI team. Bring a unit, a goal, and a few tags — leave with a working model proposal.