Solution
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.
The problem
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.
Signals we monitor
The process indicators behind every recommendation.
Intelecy reads these signals from your Historian, DCS, and SCADA — no new sensors required to get started.
How Intelecy solves it
From signal to action, mapped to the modules.
STEP 01
Reconstruct quality from process tags
Build a soft sensor that predicts lab quality in real time using the tags you already log.
STEP 02
Forecast end-of-batch state
Operators see where the batch will land long before the final sample.
STEP 03
Recommend the next setpoint
A confidence-scored move with the expected effect on yield, scrap, and throughput.
STEP 04
Explain every off-spec event
Top contributing variables and a side-by-side with a golden batch.
Expected KPIs
How customers measure success.
First-pass yield
▲%
On-spec at first sample
Defect rate
▼%
Lots requiring rework
Scrap
▼%
Of total throughput
Giveaway
▼%
Over-fill / over-quality
Proof
Nordic Foods AS
Food & beverage
“Our dryer team now adjusts setpoints when Intelecy flags drift — not when QA flags off-spec.”
Headline result
+2.1pp
first-pass yield, 6 months
Implementation
What it takes to stand this up.
Data needed
Process tags for the unit + QA results history (1–2 years) for training the soft sensor.
Timeline
Soft sensor and forecast live in 2–3 weeks, including backtest against historical batches.
Stakeholders
Process engineer, quality lead, shift supervisor, plant IT/OT.
Integration
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.