Products

Five products. One runtime, one corpus, one audit spine.

Twindra rehearses the batch, Spectryn senses it, Visqa inspects it, Conforma proves it and Kinetos moves it. Each is a land-point into a different wedge workflow, each feeds the shared quality corpus, and all five write to the same 21 CFR Part 11 audit trail.

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5 productsLine, plant and enterprise tiersOne plant-edge runtime

run_b2c7e1 · agent graph · scenario 10 nodes · 1 QA approval gate
01 · orchestrator ingest.batch_record ✓ SUCCEEDED 02 · orchestrator twin.rehearse ✓ SUCCEEDED 03 · blend_dispense blend.uniformity ✓ SUCCEEDED 04 · granulate_dry granulate.dry ✓ SUCCEEDED 05 · compress_fill compress.press ✓ SUCCEEDED 06 · coat_dry coat.spray ✓ SUCCEEDED 07 · defect_inspect inspect.tablets ✓ SUCCEEDED 08 · quality_potency spectra.release_evidence ✓ SUCCEEDED 09 · orchestrator approve.qa ◆ APPROVAL 10 · quality_conf release.genealogy ✓ SUCCEEDED

Scenario: Run one immediate-release tablet batch through granulation, compression, coating and inspection inside the approved design space, with QA approval before release. A design walkthrough, not a customer record.

The five

Each product in detail

Taglines, features and NVIDIA stack are as specified in the product briefs. Everything marked as a target is a target.

As-made batch digital twin

Twindra

Rehearse the batch before you run it.

Physics-informed, GPU-accelerated twin of a formulation and tooling family: granulation DEM, coating CFD, compression and ejection risk, dissolution surrogate. Predicts content uniformity, potency, weight and hardness, film and takt before the batch starts and hands the approved design-space envelope to the live agents.

Key features

  • Formulation- and tooling-specific twins for granulation, compression, coating and drying
  • Hit-quality-before-run optimisation of the full setpoint vector against target quality attributes and takt
  • Dissolution surrogate and content-uniformity prediction from the as-made twin
  • Rare-fault rehearsal via synthetic scenarios
  • Approved design-space export straight into the plant-edge agents
  • Versioned, change-controlled model cards for every twin

For: MSAT, formulation and process engineering teams; tech-transfer and continuous-manufacturing programmes.

Why it is different: Offline DoE and QbD tools model a single step; MES tracks history. Twindra simulates the full as-made batch at plant fidelity and feeds the result into closed-loop control.

Agent
Orchestrator (rehearsal)
NVIDIA hardware
DGX / OVX · RTX
NVIDIA SDKs
Omniverse + Replicator, CUDA DEM/CFD, Warp, Modulus, cuOpt, NIM / Triton
Runtime
NVIDIA AI Enterprise, validated on-prem deployment available
First release scope
One formulation family: granulation and compression twin, dissolution surrogate, setpoint optimiser, design-space export, validated model card. Coating and drying and more families next.
Stated as target
Surrogate accuracy targets versus wet-lab dissolution are set per family and stated as targets until fitted.
Audit
Every action to the shared 21 CFR Part 11 audit trail

Ask about Twindra Pricing

In-line spectral intelligence

Spectryn

Content uniformity and real-time release, in-line.

Fuses NIR and PAT spectra across blending, granulation, drying and compression into calibrated, formulation-specific models that call blend uniformity, content uniformity, moisture and potency in real time with confidence bounds, and builds the evidence chain for real-time release.

Key features

  • Multi-point NIR and PAT fusion across blend, granulation, drying and compression
  • Formulation-specific content-uniformity, blend, moisture and potency prediction with confidence bounds
  • Real-time-release evidence chain, method-linked, cited, Part 11 audited
  • Line-speed off-spec risk scoring and bounded setpoint recommendations
  • Chemometric model registry under GxP change control, drift detection and recalibration

For: QA and quality leads; MSAT process engineers; CDMOs needing faster, auditable release; sites pursuing RTRT and continuous manufacturing.

Why it is different: PAT and NIR vendors sell probes and monitoring. Spectryn closes the loop to a validated release decision and to control, calibrated per formulation.

Agent
Quality-and-Potency, Blend-and-Dispense
NVIDIA hardware
Jetson Orin · DGX / HGX
NVIDIA SDKs
Holoscan, TensorRT, RAPIDS / cuML, Triton + NIM
Runtime
NVIDIA AI Enterprise, validated on-prem deployment available
First release scope
One line: blend and content-uniformity NIR model with confidence bounds, off-spec risk scoring, bounded recommendations, evidence chain, Part 11 logging. Potency, moisture and RTRT next.
Stated as target
Per-formulation calibration and accuracy versus USP reference methods are targets until measured at a design partner.
Audit
Every action to the shared 21 CFR Part 11 audit trail

Ask about Spectryn Pricing

AI vision inspection

Visqa

Every tablet inspected at line speed.

Multi-camera deep models detect and classify chips, cracks, capping and lamination, spots, mottling, twins and print defects at line speed, trained on a cross-plant corpus augmented with synthetic rare faults, and tie each defect to its likely upstream cause.

Key features

  • Multi-camera chip, crack, capping, spot, mottle, twin and print detection at line speed
  • Low false-reject classification trained on a cross-plant plus synthetic defect corpus
  • Defect-to-root-cause linking (press, coater, tooling) feeding the control agents
  • Per-product model deployment with GxP change control and validated model cards
  • Human-in-the-loop review console; every correction retrains the fleet

For: OSD manufacturers and CDMOs running compression, coating and inspection lines; QA leads; MSAT; packaging and serialisation teams.

Why it is different: Rule-based inspection tunes thresholds per product and cannot learn rare faults. Visqa learns from a cross-plant and synthetic corpus and turns inspection into a closed-loop quality signal.

Agent
Defect-and-Inspect
NVIDIA hardware
Jetson Orin / Thor · DGX / HGX
NVIDIA SDKs
Metropolis / DeepStream, TensorRT, Omniverse Replicator / Cosmos, Triton + NIM
Runtime
NVIDIA AI Enterprise, validated on-prem deployment available
First release scope
One line: chip, crack and capping detection with a review console, synthetic rare-fault augmentation, validated model card, defect-to-cause hooks. More defect classes and lines next.
Stated as target
False-reject and escape-rate targets versus incumbent inspection are targets until measured.
Audit
Every action to the shared 21 CFR Part 11 audit trail

Ask about Visqa Pricing

GMP conformance and batch genealogy

Conforma

Right-first-time release, fully audited.

Ingests every action, sensor state, recommendation and QC result into a time-aligned batch genealogy, drafts deviation investigations and CAPA proposals with mandatory citations to cGMP, GAMP 5, Part 11, ICH, USP and site SOPs, and assembles the audit-ready release package for a human QA reviewer.

Key features

  • Time-aligned batch genealogy across Pildeon agents, MES, LIMS and historians
  • Retrieval-grounded deviation drafting and CAPA proposals with mandatory citations
  • Real-time off-spec and data-integrity risk flags during the run
  • Audit-ready release package assembly; immutable 21 CFR Part 11 audit trail
  • SOP-aware, permission-scoped, multi-tenant; human-in-the-loop QA approval

For: QA, quality and compliance leads; validation teams; MSAT; CDMO quality units; sites pursuing continuous manufacturing and real-time release.

Why it is different: MES and QMS systems record and route paperwork. Conforma reasons, cites and drafts, grounded in both the standards and the live batch genealogy that no standalone tool can see.

Agent
Quality-and-Conformance
NVIDIA hardware
DGX / HGX · Jetson or on-prem GPU
NVIDIA SDKs
NeMo + NeMo Guardrails, RAPIDS / cuVS, TensorRT-LLM, NIM + Triton
Runtime
NVIDIA AI Enterprise, validated on-prem deployment available
First release scope
One plant: genealogy ingest, cited deviation drafting, Part 11 audit trail, QA review console. CAPA automation, data-integrity flags and RTRT packages next.
Stated as target
Autonomy boundaries QA will approve for draft-versus-decide are agreed per site; Conforma drafts, a human decides.
Audit
Every action to the shared 21 CFR Part 11 audit trail

Ask about Conforma Pricing

Robotic material, IBC and tooling handling

Kinetos

Autonomous handling for the tablet plant.

Vision-guided mobile and arm robots dispense, transport, dock and stage IBCs, powders and compression tooling between units, sequenced by the orchestrator for the next takt, containment-aware for potent compounds, verifying material identity and genealogy at every move.

Key features

  • Vision-guided IBC, powder and tooling transport, docking and staging
  • Just-in-time material sequencing driven by the plant orchestrator
  • Material-identity and genealogy verification at every move, containment-aware
  • Sim-first training and rehearsal in Isaac before live deployment; graduated autonomy
  • Every action logged to the validated Part 11 audit trail

For: OSD manufacturers and CDMOs with high-containment, high-volume or workforce-constrained lines; operations and EHS leaders; MSAT and validation teams.

Why it is different: Handling incumbents sell fixed conveyors or generic AGVs blind to batch genealogy and containment. Kinetos is GMP-aware, genealogy-verifying and wired into the same loop as the process agents.

Agent
Robot-and-Handling
NVIDIA hardware
Jetson Thor / Orin · DGX / OVX
NVIDIA SDKs
Isaac Sim / ROS / Lab / Manipulator, Omniverse, cuOpt, TensorRT
Runtime
NVIDIA AI Enterprise, validated on-prem deployment available
First release scope
One robot cell: IBC transport and docking between two units, genealogy verification, sim-first policy, graduated autonomy, Part 11 logging. Tooling handling and whole-plant fleets next.
Stated as target
Robot hardware partners, payload and containment specifications are open; cleanroom hardening is a stated target.
Audit
Every action to the shared 21 CFR Part 11 audit trail

Ask about Kinetos Pricing

Ecosystem

How the five feed each other

The products are one system, not a portfolio. Each one's output is another one's input, and every supervised correction trains the shared corpus.

Twindra → Spectryn, Visqa

Physics makes the models trustworthy

The twin's as-made physics generate the process behaviour that Spectryn's spectral models and Visqa's synthetic defects are checked against.

Spectryn → Twindra, Conforma

Confirmed outcomes recalibrate and prove

Confirmed content-uniformity and potency outcomes recalibrate the twin and land in Conforma's release records.

Visqa → fleet

Every reviewed image trains everyone

Visqa's labelled defect stream and root-cause links feed Twindra's fault rehearsal and the control agents across lines and sites.

Conforma ← all four

The compliance conscience

Conforma turns every action from the other four into an audit-ready genealogy and a cited release package.

Kinetos ↔ all four

The hands of the loop

Kinetos moves the material between Twindra-rehearsed, Spectryn-sensed, Visqa-inspected, Conforma-logged steps, so the loop is physical end to end.

Shared

One runtime, one corpus, one spine

The same plant-edge inference runtime, the same tenant-isolated quality corpus, the same Part 11 audit spine, the same three buyers.

Compare

What is in each tier

The Line tier licenses one module on one line. Plant bundles every product and agent across the site. Enterprise adds multi-site fleets and custom models.

Products by tier
ProductLinePlantEnterprise
Twindra · batch twin·IncludedCustom formulation and tooling models
Spectryn · spectral releaseOptional (line module)IncludedIncluded
Visqa · vision inspectionOptional (line module)Included, every lineIncluded
Conforma · conformanceAudit trail and genealogyIncludedIncluded, custom SOP corpora
Kinetos · handling·IncludedCustom high-containment cells
Compression or coating controlOptional (line module)IncludedIncluded
Validated on-prem·OptionalIncluded, air-gapped option
Multi-site fleet··Included
Run timeline · scenario

The five in one batch

Twindra rehearses at step 2, Spectryn senses at steps 3 and 8, Visqa inspects at step 7, Conforma releases at step 10. Kinetos moves the IBCs between them off the critical path.

run_b2c7e1 Line 2 · rotary tablet press · scenario SUCCEEDED
  1. 01 ingest.batch_record SUCCEEDED 1.2 s

    Plant Orchestrator · Pulled the master batch record, the approved design-space limits for this formulation and tooling family, and the site autonomy policy from MES; locked the target envelope for the run.

  2. 02 twin.rehearse SUCCEEDED 41 s

    Plant Orchestrator · Twindra simulated 36 candidate setpoint vectors on the as-made batch twin (granulation DEM, compression and ejection risk, coating CFD, dissolution surrogate) and ranked them on predicted content uniformity, capping risk and takt.

  3. 03 blend.uniformity SUCCEEDED 6 m 10 s

    Blend-and-Dispense · Spectryn scored in-line NIR spectra against the formulation's chemometric model; the blend reached its uniformity end point with confidence bounds inside the site acceptance limit and blending stopped on evidence rather than on a fixed time.

  4. 04 granulate.dry SUCCEEDED 3 m 48 s

    Granulate-and-Dry · Inlet air temperature and drying airflow trimmed inside the approved window to bring loss-on-drying to the target moisture band for compression; the move was bounded, logged and inside the design space.

  5. 05 compress.press SUCCEEDED 2 m 02 s

    Compress-and-Fill · Main compression force and feed-frame speed adjusted inside the approved window to hold tablet weight and hardness on target while the twin's capping-risk estimate stayed under the site threshold.

  6. 06 coat.spray SUCCEEDED 4 m 25 s

    Coat-and-Dry · Spray rate and inlet temperature re-phased against bed temperature and exhaust humidity to hold the film-coat window and avoid twinning and mottling; no excursion from the validated recipe envelope.

  7. 07 inspect.tablets SUCCEEDED continuous

    Defect-and-Inspect · Visqa classified chips, cracks, capping, spots, mottling and print defects on six synchronised cameras, within the sub-100 ms design budget, and linked a rising chip rate to coating spray rather than to tooling.

  8. 08 spectra.release_evidence SUCCEEDED 38 s

    Quality-and-Potency · Spectryn assembled the method-linked evidence chain for content uniformity and moisture with confidence bounds and citations to the validated analytical method, ready for QA review.

  9. 09 approve.qa APPROVAL 3 m 14 s

    Plant Orchestrator · Batch release is above every autonomy level Pildeon offers. The package was held for the named QA reviewer, who approved it in the review console; the decision, the reviewer identity and the timestamp are in the Part 11 audit trail.

  10. 10 release.genealogy SUCCEEDED 52 s

    Quality-and-Conformance · Conforma wrote the time-aligned batch genealogy (every sensor state, recommendation, approved action, equipment response and QC result) and the cited release package to the immutable audit trail.

Tool calls · scenario

The agent shows its work

Every tool invocation, argument and result is written to an immutable, human-readable log, and every reasoning step is expandable. Nothing about a run is hidden from the plant or from QA.

tool-call stream · run_b2c7e1
  1. 06:12:04mes.read_batch_record({ line: "L2", product: "IR tablet · family A" })
  2. 06:12:05ok design space loaded · autonomy policy L2 · QA reviewer assigned
  3. 06:12:06twin.simulate({ candidates: 36, horizon: "full batch" })
  4. 06:12:47ok candidate #14 selected · predicted CU inside limit · capping risk under threshold
  5. 06:12:48policy.evaluate({ candidate: 14, autonomy: "L2" })
  6. 06:12:48ok 5 setpoint moves inside design space · release held for QA
  7. 06:12:49nir.score({ probe: "blender-1", model: "cu-famA-v3" })
  8. 06:18:59ok blend end point reached · confidence bounds inside acceptance limit
  9. 06:19:00plc.write_setpoint({ tag: "FBD1.INLET_T", within: "design space" })
  10. 06:22:48ok loss-on-drying in target band · move logged to audit trail
  11. 06:22:49press.write_setpoint({ tag: "TP2.MAIN_FORCE", within: "design space" })
  12. 06:24:51ok weight and hardness on target · capping risk under threshold
  13. 06:24:52vision.stream({ cameras: 6, model: "visqa-defect-v2" })
  14. 06:27:10chip rate rising on camera 4 · attributed to spray rate, not tooling
  15. 06:27:11coater.write_setpoint({ tag: "CT1.SPRAY", within: "design space" })
  16. 06:29:14ok chip rate back to baseline · film window held
  17. 06:29:15nir.evidence_chain({ attributes: ["cu", "moisture"], method: "validated" })
  18. 06:29:53ok evidence chain assembled · citations attached
  19. 06:29:54approval.request({ action: "batch_release", reviewer: "QA" })
  20. 06:33:08ok approved by the named QA reviewer · identity and timestamp logged
  21. 06:33:09audit.write_release_package({ batch: "B-240918", genealogy: "full" })
  22. 06:34:01ok scenario complete · genealogy written · package cited

Reasoning trace

Plan, thought, action, observation, exactly as the orchestrator records it.

GoalRun one immediate-release tablet batch through granulation, co…

Run one immediate-release tablet batch through granulation, compression, coating and inspection inside the approved design space, with QA approval before release.

PlanRehearse on the twin first. Stop blending on NIR evidence rath…

Rehearse on the twin first. Stop blending on NIR evidence rather than on a fixed time. Trim drying, compression and coating only inside the approved design space. Inspect continuously. Hold release for the named QA reviewer, always.

ThoughtThe fixed recipe blends for a set time regardless of what the …

The fixed recipe blends for a set time regardless of what the spectra say. On the twin, the candidate that stops on evidence and trims inlet temperature before compression keeps content uniformity and moisture inside their limits with the lowest predicted capping risk.

Actiontwin.simulate(candidates=36) → candidate #14: evidence-based b…

twin.simulate(candidates=36) → candidate #14: evidence-based blend end point, bounded drying trim, compression force held inside the window, spray rate re-phased to bed temperature.

ObservationEvery candidate that pushed turret speed for takt raised predi…

Every candidate that pushed turret speed for takt raised predicted capping risk above the site threshold and was discarded. Rate is never traded for a quality attribute.

ActionExecute candidate #14 under autonomy level L2: five setpoint m…

Execute candidate #14 under autonomy level L2: five setpoint moves inside the design space, each logged. Batch release routed to QA.

ObservationChip rate rose on camera 4 at 06:27:10. Visqa attributed it to…

Chip rate rose on camera 4 at 06:27:10. Visqa attributed it to coating spray rather than tooling; a bounded spray-rate trim returned it to baseline in about two minutes.

OutcomeScenario complete. Every move inside the approved design space…

Scenario complete. Every move inside the approved design space, the QA approval recorded with identity and timestamp, and the cited release package written by Conforma to the immutable audit trail.

Pricing

Priced per line, per plant, per group

Land on one validated workflow with a measurable baseline. Expand by line, module, site and autonomy level. Outcome components are available on yield, rejects, deviations and energy.

Line $17,000 per compression, coating or granulation line / month · billed monthly

Compression or coating control or inspection AI for one line. For OSD makers validating ROI on the wedge.

  • One line, one agent module
  • Validated plant-edge inference node
  • Equipment, PAT and MES read; bounded write inside the design space
  • Run log, Part 11 audit trail, batch genealogy
  • Business-hours support
Start a validated pilot
Recommended Plant $100,000 per plant / month · billed monthly

The whole loop: blend and dispense, granulate and dry, compress and fill, coat and dry, defect, quality and potency, yield and takt, robotic handling and the batch twin.

  • All nine agents and the orchestrator
  • Visqa inspection on every compression and coating line
  • Spectryn content uniformity and release evidence
  • Kinetos robotic IBC and tooling handling
  • Twindra as-made batch twin and campaign sequencing
  • Conforma genealogy, deviation drafting and release packages
Talk to the team
Enterprise Custom land $750k to $10M ACV

Pharma and CDMO groups standardising on Pildeon across sites, with custom formulation, tooling and tolerance models.

  • Multi-site validated plant-edge fleet
  • Custom formulation, tooling and tolerance models
  • Validated on-prem deployment, air-gapped option
  • Group autonomy policy and approval chains
  • SLAs, SSO and RBAC, dedicated engineer
Contact us

Annual prepay discounts run 15 to 20% depending on term; the toggle shows 15%. Usage metering applies above plan limits. Multi-year enterprise agreements carry usage ramps.

Guardrails

An agent that can move a compression force needs a leash

Pildeon writes to production equipment in a GMP plant. Every capability is scoped to the approved design space, every write is policy-checked, every action is written to an immutable Part 11 audit trail the site owns, and no agent can release a batch.

  • Bounded action space. Each agent can only write to an explicit setpoint allow-list, inside the limits the site's validation approved.
  • Policy engine before every write. Autonomy level, design-space limits, batch state, model version and reviewer assignment are all evaluated before a setpoint moves.
  • Human-in-the-loop gates. Batch release, any move outside the design space and any change to a validated parameter wait for a named QA reviewer.
  • Immutable audit trail. Append-only, hash-chained, exportable, retained on the site's own storage, with reviewer identity and timestamp on every decision.
  • Hard fallback. Loss of the edge node, the network or the model returns the line to the equipment's last known-good recipe.
  • Tenant and IP isolation. Formulations, recipes and models never cross a customer boundary. Validated on-prem deployment is available for regulated producers.

Compliance posture

StandardScopeStatus
21 CFR Part 11Audit trail, e-signatures, records RUNNING Designed in from day one
GAMP 5Validation package for the platform RUNNING In progress, planned for the first design partners
cGMP · ICH Q8 to Q13Design space, QbD, continuous manufacturing RUNNING Design-aligned; site validation decides
SOC 2 Type ICloud control plane QUEUED Planned, first six months
SOC 2 Type IICloud control plane QUEUED Planned, months six to twelve
Data residencyValidated on-prem, air-gapped option SUCCEEDED Available by design

Read the security and validation overview

FAQ

Straight answers

The questions site directors, MSAT engineers and QA leads actually ask in the first meeting.

Yes, but only inside an explicit setpoint allow-list bounded by the design space your validation approved, and only at the autonomy level your site has set. Level 0 is shadow mode: Pildeon observes and predicts and writes nothing. Most sites spend their first weeks there.

Get started

Start with the product that matches your wedge

Compression and inspection, blend release, coating, or tech transfer. One line, one baseline, shadow mode first.

Book a line review See pricing