Walk the plant
Every feature starts at a train with a chemist, not in a backlog.
Learn moreAbout
Medicine begins as a crystal. So do we.
Polymorra exists because the step that decides whether a drug substance is releasable — crystallization — is the least autonomous step in the entire pharmaceutical supply chain.
$24BTAM estimate
$6BSAM estimate
$360MSOM estimate
Built for the plants that make the world’s small molecules
- Branded pharma
- Generics
- CDMOs
- Fine chemicals
- Peptide APIs
- High-potency APIs
- Continuous plants
Why now
Three curves finally crossed
In-line PAT became standard in new API plants, edge GPUs became cheap enough to sit beside a crystallizer, and foundation models became good enough to reason over spectra, images, and documents together.
- PAT instrumentation is already installed and underused
- Edge inference is affordable at the train level
- Multi-modal models can fuse spectra, images, and telemetry
- Regulators now expect continuous process verification
What we believe
Autonomy has to be earned, in public
Nobody hands control of a GMP batch to software on a promise. So we built the audit trail before the autopilot, the shadow mode before the control loop, and the validation package before the sales deck.
- Perception first, control second
- Every output cited to its evidence
- Quality is a design input, not a compliance afterthought
Why it matters
The API plant is the most valuable, least autonomous step in pharma
7autonomous agents from charge to release
4PAT modalities fused in-line (Raman · FBRM · PVM · NIR)
24billion-dollar TAM across API automation and control
134% net revenue retention target as plants expand
Market figures are internal estimates from our TAM/SAM/SOM analysis. Product performance figures are design targets. [ASPIRATIONAL]
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.
Roadmap
Where we are going
Directional, and marked as such. [ASPIRATIONAL]
- Now
Crystallization wedge
Perception and assist-mode control on crystallizer trains with design partners.
- Next
Full plant loop
Reaction, isolation, drying, and review by exception across every train in a site.
- Then
Twin-led tech transfer
Lab-to-plant crystallization prediction as a standard part of route introduction.
- Later
Network autonomy
Fleet-level optimization across sites, with models that improve everywhere as each plant corrects them.
How we work
Principles we actually apply
Mark the claim
Aspirational statements are labelled. Estimates are labelled. Trust compounds.
Learn moreFail safe
If we are unsure, the DCS keeps control and the batch continues.
Learn moreSmall and deep
A few design partners with real engineering attention beats a wide pilot graveyard.
Learn moreAccelerated computing
An NVIDIA-accelerated stack, from the train to the fleet
Perception at the edge, reasoning in the cloud, and a reactor-and-crystallizer twin in between.
Stack
Jetson at the train
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
TensorRT + Holoscan
Low-latency perception and sensor-stream preprocessing for in-situ imaging and PAT spectra inside validated control windows.
Stack
Triton + NIM
Route-specific polymorph, impurity, particle-size, endpoint, and batch-reasoning models served with validated version locks and rollback.
Stack
Omniverse + Cosmos
Reactor-and-crystallizer twin plus synthesis of rare crystallization faults — oiling-out, fouling, seed drift, wrong-polymorph nucleation. [ASPIRATIONAL]
Stack
RAPIDS + cuOpt
Telemetry ETL at campaign scale, plus scheduling, solvent recovery, and crystallization-profile optimization.
Stack
NeMo + DGX
Fine-tuned chemistry-process and batch-record reasoning trained on de-identified design-partner data and chemist corrections. [ASPIRATIONAL]
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.”
Build the plant that runs itself.
Start with one crystallizer or reactor train. Prove polymorph, yield, and reprocessing ROI in a validation-friendly pilot. Then expand across the plant.