Install the edge runtime
Hardware requirements, signed image verification, network posture, and first boot at the train.
Learn moreDocumentation
Installation, connectors, agent configuration, autonomy levels, validation artifacts, and API reference — written for plant engineers and quality reviewers, not just developers.
Start here
The first hour, in order.
Hardware requirements, signed image verification, network posture, and first boot at the train.
Learn moreOPC UA and native connector setup, tag mapping, and read-only shadow configuration.
Learn moreRaman, FBRM, PVM, and NIR stream ingestion and time alignment to reactor telemetry.
Learn moreVessel geometry, agitation, jacket, and the unit operations that make up your workflow.
Learn moreBaseline capture, model evaluation, and reading the perception layer before control.
Learn moreApproval routing, chemist correction capture, and the validated control window.
Learn moreInstall
Verify the signature, pin the model bundle, and come up in shadow mode with no write authority.
$ polymorra edge install --site site-04 --train CR-2 --mode shadow
✓ image signature verified
✓ model bundle crystallize-v4.2.1 pinned [VALIDATED]
✓ opc.tcp://dcs.site-04.local:4840 connected (read-only)
✓ raman, fbrm, pvm, nir streams aligned (±40 ms)
✓ audit trail sealed and initialised
→ ready. no write authority granted.
Configuration
Train definitions, autonomy levels, and control windows live in version-controlled configuration so they can be reviewed under change control.
train: CR-2
vessel:
type: crystallizer
volume_l: 6300
agitator: retreat-curve
autonomy: assist # shadow | assist | bounded
agents: [crystallize-and-seed, purity-and-polymorph]
windows:
jacket_ramp_c_per_min: { min: 0.05, max: 0.35 }
antisolvent_ml_per_min: { min: 0, max: 900 }
approval:
required_roles: [process-chemist]
esignature: true
audit: part-11
Reference
Every section includes the quality artifacts, not only the technical ones.
Reference
Configuration, inputs, bounded actions, and evaluation metrics for each of the seven agents.
Reference
DeltaV, PCS 7, Experion, OPC UA, AutoChem, PI, PAS-X, Syncade, and LIMS.
Reference
Levels, control windows, approval routing, override, and fail-safe behaviour.
Reference
Model setup, calibration from historical campaigns, and scale-up workflows.
Reference
Audit schema, evidence linking, exception review, and export formats.
Reference
IQ/OQ/PQ templates, model version control, and change-control artifacts.
For quality
Validation packages, model provenance records, and audit-trail specifications are first-class documentation — not an appendix produced during a panic before an inspection.
Plant-edge console
Supersaturation, particle size distribution, predicted polymorph, impurity trajectory, and the next control move — with the evidence behind each number.
Polymorra plant-edge console showing live supersaturation, particle size distribution, predicted polymorphic form, and impurity trajectory for a running crystallization, with the recommended next control move.
Integrations
Polymorra reads and writes through the reactor DCS, PAT instruments, crystallizer and isolation skids, historians, and MES you already validated.
Trust & compliance
Polymorra is engineered for regulated drug-substance production: grounded outputs, immutable audit trails, graduated autonomy, and validation documentation from day one.
Immutable, validated audit log for every agent perception, recommendation, and control action, with e-signature-ready review flows.
Change control, validated model versioning, and deployment documentation designed for drug-substance manufacturing.
SOC 2 Type I in progress, Type II on the roadmap; SSO/RBAC, encryption in transit and at rest. [ASPIRATIONAL]
Per-tenant isolation of routes, recipes, spectra, and crystal images. No cross-tenant training. On-prem option.
Shadow → assist → bounded auto-control. Every autonomy level is explicitly configured, bounded, and revocable.
Every recommendation cites the spectra, images, telemetry, SOP, or specification it was derived from.
Inference runs on-site so control loops survive network loss; cloud is for training, fleet, and analytics.
Chemist and QA checkpoints are first-class: approve, correct, or reject — and every correction trains the models.
Accelerated computing
Perception at the edge, reasoning in the cloud, and a reactor-and-crystallizer twin in between.
Stack
Plant-edge inference per reactor or crystallizer train: PVM crystal-image models, FBRM feature extraction, Raman and NIR soft sensors, anomaly and safety classifiers. [ASPIRATIONAL]
Stack
Low-latency perception and sensor-stream preprocessing for in-situ imaging and PAT spectra inside validated control windows.
Stack
Route-specific polymorph, impurity, particle-size, endpoint, and batch-reasoning models served with validated version locks and rollback.
Stack
Reactor-and-crystallizer twin plus synthesis of rare crystallization faults — oiling-out, fouling, seed drift, wrong-polymorph nucleation. [ASPIRATIONAL]
Stack
Telemetry ETL at campaign scale, plus scheduling, solvent recovery, and crystallization-profile optimization.
Stack
Fine-tuned chemistry-process and batch-record reasoning trained on de-identified design-partner data and chemist corrections. [ASPIRATIONAL]
FAQ
Both — in a deliberate order. Polymorra starts in shadow mode (perceive only), moves to assist (recommend, chemist approves), then to bounded auto-control on qualified trains where setpoint moves stay inside validated control windows. Every level is configured per train and revocable.
Polymorra is built for GMP (ICH Q7) and 21 CFR Part 11: immutable audit trail, validated model version locks with rollback, change control, grounded and citable outputs, and human-in-the-loop checkpoints. We ship a validation package and support IQ/OQ/PQ activities with your quality unit.
API or OPC access to your reactor DCS, PAT instruments (Raman, FBRM, PVM, NIR), crystallizer and isolation skids, and MES or historian — plus a train with enough campaign volume for clear ROI.
No. Per-tenant isolation of routes, recipes, spectra, and crystal images is enforced at the data and model layer. Cross-site benchmarks are opt-in and de-identified.
A typical wedge pilot instruments one train, runs shadow mode over a baseline campaign, then moves to assist mode with a defined polymorph, yield, or reprocessing success metric agreed up front.
Perception and control inference run at the plant edge. Cloud handles training, fleet management, and analytics — losing it degrades reporting, not the batch.
Start with one crystallizer or reactor train. Prove polymorph, yield, and reprocessing ROI in a validation-friendly pilot. Then expand across the plant.