Improve quality and yield with no-code Industrial AI.
Quality drift rarely has one cause. Intelecy models the full process context, so engineers see which variables are pulling the batch off-spec — and what to change next.
What makes this operationally expensive today.
Batch variability
Same recipe, different results — operators tune by feel instead of by signal.
Off-spec product
Quality lab confirms the problem hours after the batch is already off.
Scrap and rework
Rework eats throughput; customer complaints follow the bad lots.
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 01Model builder →
Reconstruct quality from process tags
Build a soft sensor that predicts lab quality in real time using the tags you already log.
- STEP 02Forecasting →
Forecast end-of-batch state
Operators see where the batch will land long before the final sample.
- STEP 03Prescriptive optimization →
Recommend the next setpoint
A confidence-scored move with the expected effect on yield, scrap, and throughput.
- STEP 04Data Explorer →
Explain every off-spec event
Top contributing variables and a side-by-side with a golden batch.
How customers measure success.
Nordic Foods AS
"Our dryer team now adjusts setpoints when Intelecy flags drift — not when QA flags off-spec."
What it takes to stand this up.
Process tags for the unit + QA results history (1–2 years) for training the soft sensor.
Soft sensor and forecast live in 2–3 weeks, including backtest against historical batches.
Process engineer, quality lead, shift supervisor, plant IT/OT.
Reads Historian and LIMS exports; recommendations served in the workspace or pushed to the HMI via API.
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.