The hardest number to get in a chemical plant is the one that decides whether the batch is good. Purity, color, viscosity, moisture, assay — these come from a sample pulled, walked to the QC lab, queued, and analyzed, and by the time the result lands the batch has moved on. If it drifted off-spec an hour ago, you are finding out now, after the rework is already baked in. Process Analytical Technology was built on a simple reversal of that logic: quality should be ensured throughout the process, not verified after it. Quality prediction makes that real — using process and spectral data to predict the lab result in real time, so the answer arrives while you can still act on it, on-prem and 21 CFR Part 11 ready.
iFactory Quality / PAT Suite
Predict Quality in Real Time, Not After the Lab
Predict purity, color, viscosity, moisture and assay live from process and spectral data — PAT soft sensors that catch off-spec before it happens, on a single on-prem server, 21 CFR Part 11 ready.
Real-time
quality, not lab-delayed
5
attributes predicted live
Part 11
audit trail ready
On-prem
inside your firewall
The Lab Delay Is Where Batches Go Off-Spec
The lack of a reliable online quality measurement is one of the oldest problems in process manufacturing. Sampling, transport, and analysis all take time, and that gap is exactly when a batch can drift, foam, or wander off target without anyone knowing. Operators end up steering by a result that describes the past, not the present.
Delay
Sample to result
Pulling a sample, sending it to the lab, and waiting for analysis means the answer always lags the process.
Blind
Between samples
Between lab checks the batch runs unmeasured, so drift develops silently until the next result confirms it.
Rework
Already locked in
By the time off-spec is confirmed, the material exists — now it is scrap, rework, or a costly disposition.
Start-ups
Worst exposure
Grade changes and start-ups produce the most off-spec, exactly when lab feedback is slowest to keep up.
Soft Sensors: The Lab Result, Predicted
A soft sensor is an inferential measurement — it estimates a quality attribute you cannot easily measure online from the process and spectral signals you can. By pairing in-line spectroscopy with chemometric models, iFactory predicts the value the lab would report, continuously, closing the gap that sampling leaves open.
NIR Spectroscopy
Near-infrared reads molecular overtones of C-H, O-H, and N-H bonds, ideal for moisture, composition, and concentration.
FTIR & Raman
Complementary in-situ methods — FTIR for the solution phase, Raman for crystallization and solids — for full reaction insight.
Chemometric Models
PLS and PCA turn rich spectra into hard numbers, with prediction errors reported in the low single-digit percent.
Lab-Synchronized
Each lab result feeds back to keep the soft sensor aligned, correcting for the time delay between sample and signal.
Want to see a soft sensor predict one of your attributes against your own lab data? Book a demo and we'll model a real example live.
The Five Attributes, Live
The attributes that gate a batch release are exactly the ones a soft sensor can predict from process data. Instead of waiting for each to come back from the lab, you watch them trend in real time and intervene before any one of them crosses spec.
Purity
Track concentration and impurity trends continuously so a drift away from target is visible while it is still correctable.
Color
Predict color development from spectral signature, catching off-color batches before they reach final QC.
Viscosity
Infer viscosity from process behavior in real time, a property otherwise slow and awkward to measure off-line.
Moisture
NIR is especially strong on moisture and water content, giving an immediate read where lab Karl Fischer lags.
Assay
Estimate active assay continuously so the headline release number is trending toward spec, not discovered at the end.
All At Once
Multi-attribute prediction runs in parallel, giving one live quality picture instead of five separate lab queues.
Every Batch Against the Golden Batch
Your best runs hold a pattern worth repeating. iFactory captures that as a golden-batch fingerprint and scores every new batch against it in real time, surfacing the subtle multivariate drift no single trend chart would show — so a promising run gets corrected onto trajectory instead of cascading into rework.
Golden Fingerprint
A validated reference trajectory built from your best historical runs becomes the target every new batch is measured against.
Live Similarity Score
Each batch gets a real-time score against the fingerprint; a drop below threshold triggers attention before quality is lost.
Multivariate SPC
Statistical monitoring across many variables at once detects deviations — like an emerging foaming problem — early.
Correct On Trajectory
Deviating batches are flagged while there is still time to adjust setpoints and pull them back toward the golden path.
Curious what your golden batch looks like as a fingerprint? Talk to our quality team and we'll map it from your historian data.
Built for PAT and 21 CFR Part 11
Prediction is only useful in a regulated plant if the record behind it holds up. Because soft-sensor output is quality data, it falls under the same expectations as any GMP record. iFactory is built around ALCOA+ data integrity, so every prediction, model version, and disposition is attributable, traceable, and ready for inspection — supporting real-time release thinking instead of end-of-line-only testing.
Full Audit Trail
Every prediction and model change time-stamped and retained, aligned with 21 CFR Part 11 electronic-record expectations.
Electronic Signatures
Controlled access and e-signatures on model updates and dispositions, so changes carry accountability.
ALCOA+ Integrity
Records that are attributable, legible, contemporaneous, original, and accurate, satisfying GMP and predicate rules.
Real-Time Release Ready
Predicting quality from process data supports release based on process understanding, not only post-production tests.
On-Prem AI, Inside Your Firewall
Process recipes, spectral libraries, and quality models are core chemical-plant IP. The iFactory AI runs on a pre-configured edge server on-premise, with all processing inside your firewall and no external egress required to operate. The integration with historian and instrument data is read-only by design, so there is no path from outside into your control systems — your data and your models stay in the building.
All Processing On-Site
Prediction and golden-batch scoring run on the in-plant edge server; nothing about your process leaves the building.
Read-Only Integration
Connections to historian, DCS, and instruments are inbound only — no external egress, no path into control from outside.
Starts With Your Data
Models learn from the historian and lab data you already capture; perfect data readiness is not a prerequisite.
Your Models Stay Yours
Spectral libraries and quality models are sensitive IP and remain on-prem, satisfying data residency by architecture.
What Real-Time Quality Delivers
Predicting quality instead of waiting for it converts directly into fewer off-spec batches, faster release, and tighter consistency. These reflect outcomes chemical manufacturers report after pairing PAT soft sensors with golden-batch analytics.
Fewer
Off-spec batches
drift caught and corrected before material goes out of spec
Tighter
Around golden
quality distribution pulled in around golden-batch targets
Faster
To release
process-based prediction reduces waiting on the QC queue
Earlier
Deviation caught
multivariate signals surface problems well before lab results
Curious how much of your off-spec is predictable in advance? Talk to our quality team and benchmark your batches against PAT prediction.
Frequently Asked Questions
How can software predict purity or assay without a lab test?
Through soft sensors — inferential models that estimate a hard-to-measure quality attribute from the process and spectral data you can measure online. By pairing in-line NIR, FTIR, or Raman spectroscopy with chemometric models like PLS and PCA, the system predicts the value the lab would report, continuously. Reported prediction errors sit in the low single-digit percent, and each lab result feeds back to keep the model aligned.
Which attributes can it predict?
The ones that gate batch release: purity, color, viscosity, moisture, and assay, all predicted in parallel for one live quality picture. NIR is particularly strong on moisture and composition, while FTIR and Raman add reaction and crystallization insight. Instead of five separate lab queues, you watch all five trend in real time and act before any crosses spec.
What is a golden-batch fingerprint?
It is a validated reference trajectory built from your best historical runs. Every new batch is scored against it in real time, producing a similarity score that drops when the run drifts. Multivariate statistical monitoring across many variables catches subtle deviations — an emerging foaming problem, for instance — early enough to adjust setpoints and pull the batch back onto the golden path before it becomes rework.
Is it 21 CFR Part 11 and PAT compliant?
It is built for it. The system maintains full audit trails, supports electronic signatures and controlled access, and follows ALCOA+ data-integrity principles, so every prediction and model version is attributable and inspection-ready. Predicting quality from process data is the core PAT idea — ensuring quality throughout the process rather than only by post-production testing — which supports real-time release thinking.
Does our process data or models leave the plant, and how do we start?
No data leaves. The AI runs on a pre-configured edge server on-premise with all processing inside your firewall and no external egress, and the integration with historian and instruments is read-only. Models start from the historian and lab data you already capture — perfect data readiness is not required. The fastest way to see fit is a demo on one of your attributes; book a slot and bring the quality measurement you most want predicted live.
Know the Result Before the Lab Does.
See Quality Prediction on Your Own Process
Bring one attribute that keeps you waiting on the lab — purity, color, viscosity, moisture, or assay. We'll model a soft sensor against your data, show golden-batch scoring catching drift in real time, and demonstrate the Part 11 audit trail — all on an on-prem server inside your firewall.
Predict
5 attributes live
On-prem
inside your firewall
