21 CFR Part 11
Immutable, validated audit log for every agent perception, recommendation, and control action, with e-signature-ready review flows.
Case studies
Scenarios, stated honestly, until batches say more.
We are pre-commercial. The scenarios below describe how Polymorra is deployed and what each engagement is measured against — they are illustrative, not published customer results. [PLACEHOLDER]
Built for the plants that make the world’s small molecules
- Branded pharma
- Generics
- CDMOs
- Fine chemicals
- Peptide APIs
- High-potency APIs
- Continuous plants
Scenario 01 · Branded pharma
A polymorph that will not stay put
A high-value API where Form II converts under certain drying conditions. Polymorra predicts form in-line during crystallization, steers the antisolvent addition away from the transition boundary, and monitors form through drying.
- Measured on: polymorph consistency across the campaign
- Agents: crystallize-and-seed, purity-and-polymorph, isolate-and-dry
- Autonomy: shadow → assist over one campaign
Scenario 02 · Generics
Yield left in the mother liquor
A high-volume generic API where cooling profile conservatism protects quality at the cost of yield. Polymorra holds supersaturation closer to the metastable limit with live feedback rather than margin.
- Measured on: yield per batch and solvent recovery
- Agents: crystallize-and-seed, flow-and-continuous
- Autonomy: assist → bounded auto-control on a qualified train
Scenario 03 · CDMO
Tech transfer that keeps slipping
A CDMO receiving a client route needs to reproduce a lab crystallization at plant scale. The twin predicts plant behaviour from lab data, and shadow mode validates the prediction on the first campaign.
- Measured on: time to first in-spec plant batch
- Agents: twin, crystallize-and-seed, purity-and-polymorph
- Autonomy: shadow, with the twin used pre-campaign
Scenario 04 · Quality
Release that waits on reviewers
A site where batch-record review is the release bottleneck. The record-and-review agent surfaces only deviations, each linked to the spectra, images, and telemetry behind it.
- Measured on: review hours per batch and deviation cycle time
- Agents: record-and-review, API-knowledge
- Autonomy: no control authority required
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.
Pilot design
How we agree success before we start
Every engagement fixes the metric, the baseline, and the review cadence in writing.
| Scenario | Primary metric | Baseline | Decision point |
|---|---|---|---|
| Polymorph control | Form consistency | Prior campaign | End of shadow campaign |
| Yield & solvent | Yield per batch | Trailing 12 batches | After 10 assisted batches |
| Tech transfer | Time to in-spec batch | Site historical average | First plant campaign |
| Batch review | Review hours per batch | Current QA log | 30 days of records |
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.”
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.
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.
Pricing
Land on one train. Expand to the plant.
Pricing follows the value of the batch: per reactor or crystallizer train, per plant, or an enterprise agreement with outcome components.
Line
Manufacturers validating ROI on the wedge workflow.
$13,000
per reactor or crystallizer train / month
- Reaction-control, crystallization/polymorph-control, or PAT-sensing AI for one train
- Plant-edge runtime with validated model versioning
- Connectors for one DCS, one PAT stack, and one MES
- Review console for chemists and PAT engineers
- Part 11 audit trail from day one
Most popular
Plant
Scaling plants adopting the full autonomous loop.
$85,000
per plant / month
- React-and-charge + crystallize-and-seed agents
- Purity-and-polymorph PAT sensing across trains
- Isolate-and-dry plus flow-and-continuous optimization
- Batch-record review by exception
- Reactor-and-crystallizer digital twin
- Campaign scheduling and solvent-recovery optimization
Enterprise
CDMOs and pharma networks standardizing on Polymorra.
Custom
land $600k – $7M ACV
- Multi-site deployment and plant-edge fleet management
- Custom route, crystallization, and impurity models
- Validation support and qualification documentation
- On-prem or hybrid deployment with NVIDIA AI Enterprise
- SLAs, dedicated solutions engineering, and outcome pricing
Write the next case study with us.
Start with one crystallizer or reactor train. Prove polymorph, yield, and reprocessing ROI in a validation-friendly pilot. Then expand across the plant.