USE CASES

What your plant can predict from data it already has.

A manufacturer can predict scrap, defect risk, machine downtime, and yield from data it already collects: historian exports, MES and QMS extracts, and inspection spreadsheets. Cogentic trains those models on your machine, inside your network: no data scientist, no cloud. Upload a file, pick the column to predict, and have a first model scored against held-out data the same day.

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CORE SCENARIOS

The four models most plants ship first. Each starts from a file you can export today.

No new sensors, no new pipeline. Every scenario below trains on exports your plant already produces, and changes a decision an engineer makes every week.

Scrap prediction

Know which runs will end up in the bin before you commit material.

Prediction target: scrap_flagpass / scrap outcome, per run or lot
Data you already have: Historian exports of process parameters, run logs from your MES, and the scrap disposition column your team already records.
Monday morning: Hold or adjust the risky run before material is committed. You scrap a setting, not a pallet of parts.

Defect risk ranking

Rank every unit or lot by its probability of failing inspection.

Prediction target: defect_riskprobability of failing inspection, per unit or lot
Data you already have: QMS inspection records, end-of-line test results, and the lot-genealogy spreadsheets your quality team already keeps.
Monday morning: Inspect from the top of the ranked list. QC hours go to the handful of lots carrying most of the risk, not a flat sample.

Downtime prediction

Flag the assets most likely to fail inside your planning window.

Prediction target: fails_in_30dwill this asset fail within the horizon you plan against
Data you already have: Historian sensor trends, alarm and event logs, and the work-order history sitting in your CMMS.
Monday morning: Write the work order for the next planned stop instead of losing a shift to a failure nobody saw coming.

Yield optimization

See which process parameters actually move first-pass yield.

Prediction target: first_pass_yieldpredicted yield for a given set of process conditions
Data you already have: Process parameters from the historian, batch records, and the first-pass-yield column in your MES extract.
Monday morning: Tighten the operating window around the parameters that matter and stop chasing the ones that don't.

IN PRODUCTION

Running at a Fortune 100 manufacturer. Under NDA, so no logo. Here's the shape of it.

The scenario

A Fortune 100 manufacturer wanted to predict quality outcomes on a production line. Process and inspection data could not leave the network, which disqualified cloud AutoML before a demo was ever booked.

What we did

Deployed Cogentic on a machine inside their environment and trained on extracts their engineers already pulled: no new pipeline, no data leaving the building. We tailored the deployment to their line and handed their team the keys.

The outcome

In production today. Their engineers run and retrain the models themselves on new data, with every run reproducible end to end. No data scientist on the line.

Read the full case study

Manufacturing-first. The same pattern holds wherever the data can't leave the building.

manufacturing · medical devices · semiconductors · automotive & EV · energy & utilities · industrial equipment

GET STARTED

Bring your data. We'll show you what it can predict.

Bring one export from your historian, MES, or QMS. On the call we'll map it to one of these four scenarios and show you what a first model looks like in an environment you control: no cloud, no commitment.

Book a demo