How a Fortune 100 manufacturer cut scrap on a critical line with no data-science team, and no data ever leaving the network.
A process engineer uploaded data the plant already had, cleaned it in a guided wizard, and trained a model that flags at-risk parts before they become scrap. Every step ran on their own hardware, inside their own network.
Representative figures. Customer anonymized under NDA.
Scrap was caught too late, and every fix they could buy meant sending the data away.
On one high-value line, defects surfaced at final inspection, after material, machine time, and labor were spent. Each scrapped part was a write-off, and a defect that escaped to a customer cost far more. Engineers spent days combing spreadsheets for the upstream conditions behind bad parts.
They knew the answer was predictive. The problem: every path to it ran off their network, and that was not allowed.
Cloud AutoML platforms
Data could not leave the network. A hard line, not a preference.
Hiring a data-science team
Too slow and costly for a single line, with no guarantee it would stick.
Existing SPC dashboards
Told them what already went wrong. They needed to see it coming.
One engineer, one app, from raw records to a working model, all on-prem.
Cogentic was deployed inside the customer's network. Upload, profile, clean, train, and predict happened in one place, run by a process engineer. No code was written, and no data was moved.
1 · Uploaded
The engineer dropped in records the plant already kept. No new instrumentation, no export anywhere.
2 · Profiled
Cogentic read every column, types, distributions, and missing values, so the team saw what they were working with.
3 · Cleaned
A guided wizard fixed types, ranges, and gaps, recording every choice as a reproducible plan.
4 · Trained
They picked the outcome to predict. Cogentic trained, tuned, and selected the strongest model.
5 · Predicted
The model scored in-process parts and flagged likely failures, with confidence scores, before inspection.
Scrap they stopped making, and a boundary they never had to cross.
The clearest win was the loss that stopped happening: parts no longer scrapped, and defects that no longer escaped to a customer. The second win mattered most to their security team. None of it required moving the data.
Fewer parts written off on the pilot line after the model went live.
At-risk parts flagged before final inspection, not after shipment.
On held-out data the team could re-check. Not a black box.
First validated model in three weeks, not the quarters a hire takes.
The data never left their network. Training, prediction, and results all stayed on their hardware.
“We'd been told predictive quality meant the cloud and a data-science hire. We got a working model in weeks, on our own hardware, run by one of our own engineers, and nothing we own ever went anywhere.”
See what predictive quality could be worth on your own data.
Book a working session and we'll walk the same path on your data, your hardware, your network. Or estimate the savings first.