Autonomous crystallization control: a reference architecture
How perception, planning, control, and record-keeping fit together around a crystallizer, and where each one fails without the others.
ReadResources
Everything we learned walking API trains.
Practical material for manufacturing, science, and quality teams evaluating autonomous control of drug-substance production.
Built for the plants that make the world’s small molecules
- Branded pharma
- Generics
- CDMOs
- Fine chemicals
- Peptide APIs
- High-potency APIs
- Continuous plants
Whitepapers
Deep dives
Long-form technical material. [PLACEHOLDER — publication in progress]
Fusing Raman, FBRM, PVM, and NIR
Why single-modality PAT under-determines the crystallization state, and how disagreement between instruments becomes signal.
ReadValidating AI in a GMP plant
Version locks, change control, audit trails, and the questions your quality unit will ask on the first call.
ReadTemplates
Take these into your next meeting
Editable artifacts for scoping and approving a pilot.
Pilot scoping worksheet
Train selection, metric definition, baseline period, and decision criteria on one page.
ReadPAT readiness checklist
What instruments, tags, and access a train needs before a shadow campaign can start.
ReadQA question bank
The questions to ask any AI vendor about audit trails, autonomy, and model version control.
ReadBuyer’s guide
How to evaluate autonomous control honestly
Most industrial AI in pharma is analytics with a chat box. The separating questions are simple: does it perceive the crystal, does it act, and can quality approve what it did?
- Does it fuse in-line PAT or only read historian tags?
- Does it write setpoints, or only produce advice?
- Is every output cited to spectra, images, or documents?
- Is the audit trail Part 11-ready, or an application log?
Landscape
Where Polymorra sits
A simplified view of the categories teams compare us against.
| Category | Perceives the crystal | Acts on the plant | GMP record |
|---|---|---|---|
| DCS / automation platforms | No | Yes (fixed logic) | Partial |
| PAT software suites | Yes (single modality) | No | Partial |
| Industrial AI analytics | No | No | No |
| MES / eBR systems | No | No | Yes |
| Polymorra | Yes (fused) | Yes (bounded) | Yes (Part 11) |
The autonomous loop
Perceive, plan, act, sense, optimize, log
Polymorra closes the loop around the reaction mass and the crystal — the two things a conventional DCS cannot actually see.
Perceive
Fuse Raman, FBRM, PVM in-situ imaging, NIR, and reactor telemetry into a live picture of the reaction mass, slurry, and solids.
Plan
Plan the react-and-charge, crystallize-and-seed, and isolate-and-dry moves for this batch against the target polymorph, particle size, and purity.
Act
Run with adaptive control: cooling and antisolvent profile, seeding, supersaturation, agitation, addition rate, and endpoint.
Sense & predict
Predict polymorph, particle size and habit, impurity profile, and yield in-line — before offline XRPD and HPLC confirm it.
Optimize
Optimize yield, solvent use, cycle time, and reprocessing risk across the campaign, then flag off-spec and polymorph risk early.
Log & retrain
Write an immutable Part 11 record; every chemist correction trains the site model and compounds the data moat.
Plant-edge console
One screen for the batch that is running right now
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
Connects to the systems already running your plant
Polymorra reads and writes through the reactor DCS, PAT instruments, crystallizer and isolation skids, historians, and MES you already validated.
Reactor control & DCS
Emerson DeltaVSiemens PCS 7Honeywell ExperionOPC UAModbus/TCP
PAT & analytics
Mettler-Toledo FBRMPVM in-situ imagingReactIREndress+Hauser RamanNIRHPLC / XRPD imports
Isolation & drying
Nutsche filter-dryersCentrifugesAgitated dryersConical millsMicronizers
MES, historian & quality
Körber PAS-XEmerson SyncadeOSIsoft PILIMSeBR / QMS exports
Trust & compliance
Validated for GMP manufacturing
Polymorra is engineered for regulated drug-substance production: grounded outputs, immutable audit trails, graduated autonomy, and validation documentation from day one.
21 CFR Part 11
Immutable, validated audit log for every agent perception, recommendation, and control action, with e-signature-ready review flows.
GMP / ICH Q7
Change control, validated model versioning, and deployment documentation designed for drug-substance manufacturing.
SOC 2
SOC 2 Type I in progress, Type II on the roadmap; SSO/RBAC, encryption in transit and at rest. [ASPIRATIONAL]
Route & IP protection
Per-tenant isolation of routes, recipes, spectra, and crystal images. No cross-tenant training. On-prem option.
Graduated autonomy
Shadow → assist → bounded auto-control. Every autonomy level is explicitly configured, bounded, and revocable.
Grounded outputs
Every recommendation cites the spectra, images, telemetry, SOP, or specification it was derived from.
Plant-edge first
Inference runs on-site so control loops survive network loss; cloud is for training, fleet, and analytics.
Human-in-the-loop
Chemist and QA checkpoints are first-class: approve, correct, or reject — and every correction trains the models.
Voices from the plant
What operators say
Composite quotes from design-partner discovery interviews. [PLACEHOLDER — to be replaced with named references post-pilot]
“The crystallization step is where our campaigns live or die. Seeing supersaturation, particle size, and predicted polymorph in one place — and having the profile adjust itself — is what we have wanted for twenty years.”
“We do not want another dashboard. We want the batch to come out right and the record to be reviewable by exception. That is the only pitch that gets past QA.”
“Tech transfer is our bottleneck. If the twin can predict the plant crystal from the lab crystal, that is worth more than the software costs.”
Bring the checklist. We will answer all of it.
Start with one crystallizer or reactor train. Prove polymorph, yield, and reprocessing ROI in a validation-friendly pilot. Then expand across the plant.