Multivariate analysis, made usable

Turn your process data into clear, actionable answers

ProcessLens applies PCA, PLS, and MSPC to your batch and sensor data — so your R&D and QC team can see what's really driving quality and catch problems before they become failures. No statistics PhD required.

Try the demo — no account needed See how it works →

Score plot — batch quality PCA

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What ProcessLens does

A process line or QC lab produces hundreds of correlated data points per batch — too many to review manually, and too specialized for spreadsheets. ProcessLens is a web platform your team can log into and run multivariate analysis on that data, without a chemometrics degree.

Data volume

Hundreds of correlated sensor tags per batch make manual review impossible.

Specialist tooling

Chemometrics software is built for statisticians, not process engineers.

Cost

Enterprise chemometrics suites price out mid-sized R&D and QC teams.

How it works

1

Upload your dataset

CSV, Excel, or a direct historian connection.

2

Choose your analysis

PCA, PLS, or MSPC — guided by your question, not the math.

3

Read your results

Score plots, VIP scores, and control limits explained in plain language.

Core analytical methods

PCA

Pattern detection

Reduce dozens of correlated variables into a handful of components that capture the real story in your data.

PLS

Quality prediction

Predict quality outputs from process inputs — before the batch finishes.

MSPC

Real-time monitoring

Catch correlated deviations and faults long before a single-variable chart would.

"We caught a cooling-water fault 40 minutes before it would have failed a batch."

— Illustrative example, not an actual customer quote yet

Simple, token-based pricing

Per-user subscriptions with a monthly token allocation. Light analyses cost fewer tokens; heavy computation costs more — no unlimited tier, no bulk-discount maze.

See plans & token model →

Start with your own data

Try the sandboxed demo first — no account needed. Then request a one-week free trial with your own data.