Line continues on last known good
Alarm raised, audit gap recorded, sync on recovery.
FAQ
The questions that actually come up in the first meeting.
Platform and deployment, safety and data, commercial. If the answer is not here, ask; if we do not know yet, we will say so.
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.
FAQ
Platform and deployment
What the platform controls, what goes in the plant and what it connects to.
Blending end point, granulation and drying setpoints, compression force and feed-frame speed, coating spray and inlet temperature, inspection thresholds via model cards, and robotic material movement, each inside the design space the site validated and at the autonomy level the site set.
No. It connects to them, reads continuously and writes genealogy and release packages back to them. It is a supervisory layer, not a system of record.
A Jetson Orin or Thor, or an on-prem GPU node, per line, on an isolated OT segment. Training and simulation run on DGX, HGX or OVX, on-prem or in the cloud by your choice.
Enough batch records, QC results and deviations on the wedge workflow to baseline and to fit per-tenant models. The shadow-mode period adds live data. Exact needs depend on the formulation family and are agreed in scoping.
Presses, coaters, granulators and blenders over OPC UA and PLC tags, PAT and NIR over vendor SDKs, vision over GigE or existing exports, MES and LIMS over REST. Connectors are built against design partners' stacks.
Yes. Validated on-prem deployment with an air-gapped plant edge, including training, is available for sites whose data cannot leave the plant.
FAQ
Safety, validation and data
Design space, release, failure and data residency.
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.
No. The approved design space is a wall the policy engine enforces at call time. A move outside it, or a change to a validated parameter, is a change-control event that waits for QA sign-off. No autonomy level removes that gate.
A named QA reviewer, every time. Pildeon assembles the evidence chain and the cited release package; the reviewer approves or rejects it in the review console, and the decision, identity and timestamp land in the Part 11 audit trail.
The line returns to the equipment's own last known-good recipe. Pildeon is a supervisory layer on top of your existing control systems, never a replacement, so an outage degrades the line to the way it runs today, not to a stop.
Only if you choose cloud training. Formulations, recipes, spectra and defect libraries are tenant-isolated and never used to train another customer's models. Validated on-prem deployment with an air-gapped plant edge is available.
Shadow mode on one wedge workflow first, with baseline rejects, deviations, yield, energy and release time captured before any autonomous action. Assist mode once prediction accuracy is shown against QC results. A paid validated pilot only when there is a written ROI target. Timelines depend on the site's validation and change-control calendar.
FAQ
Commercial
Price, trial, ownership, risk and how to start.
Line tier $17,000 per compression, coating or granulation line per month; Plant tier $100,000 per month for the whole loop; Enterprise custom, landing between $750k and $10M annual contract value. Annual prepay saves 15 to 20%. Outcome components are available.
Shadow-mode instrumentation for design partners is at no charge. A paid validated pilot follows only when there is a written ROI target.
You own your data. Models fitted on your data are yours to use inside Pildeon and are never used for another tenant. Validation artefacts are delivered to you.
The pharma sales and validation cycle is long; robotics is capital-intensive; per-formulation calibration and surrogate accuracy are targets until measured. We mitigate with a shadow-to-pilot motion and staged autonomy, and we say so.
You keep the baseline report, the validation artefacts produced so far and your data. Connectors are removed under change control.
Book a line review. Bring the line, the number that hurts and the names of your MSAT champion and QA reviewer.
Run timeline · scenario
The run behind most answers
When an answer says step 5 or the approval gate, it means this scenario run.
run_b2c7e1
Line 2 · rotary tablet press · scenario
SUCCEEDED
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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
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.
- 06:12:04mes.read_batch_record({ line: "L2", product: "IR tablet · family A" })
- 06:12:05ok design space loaded · autonomy policy L2 · QA reviewer assigned
- 06:12:06twin.simulate({ candidates: 36, horizon: "full batch" })
- 06:12:47ok candidate #14 selected · predicted CU inside limit · capping risk under threshold
- 06:12:48policy.evaluate({ candidate: 14, autonomy: "L2" })
- 06:12:48ok 5 setpoint moves inside design space · release held for QA
- 06:12:49nir.score({ probe: "blender-1", model: "cu-famA-v3" })
- 06:18:59ok blend end point reached · confidence bounds inside acceptance limit
- 06:19:00plc.write_setpoint({ tag: "FBD1.INLET_T", within: "design space" })
- 06:22:48ok loss-on-drying in target band · move logged to audit trail
- 06:22:49press.write_setpoint({ tag: "TP2.MAIN_FORCE", within: "design space" })
- 06:24:51ok weight and hardness on target · capping risk under threshold
- 06:24:52vision.stream({ cameras: 6, model: "visqa-defect-v2" })
- 06:27:10chip rate rising on camera 4 · attributed to spray rate, not tooling
- 06:27:11coater.write_setpoint({ tag: "CT1.SPRAY", within: "design space" })
- 06:29:14ok chip rate back to baseline · film window held
- 06:29:15nir.evidence_chain({ attributes: ["cu", "moisture"], method: "validated" })
- 06:29:53ok evidence chain assembled · citations attached
- 06:29:54approval.request({ action: "batch_release", reviewer: "QA" })
- 06:33:08ok approved by the named QA reviewer · identity and timestamp logged
- 06:33:09audit.write_release_package({ batch: "B-240918", genealogy: "full" })
- 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.
Autonomy
Four levels, set per agent and per setpoint
A tablet plant does not go from fixed recipes to autonomy in one step, and validated parameters never change without sign-off. Pildeon makes the level explicit, auditable and reversible at any time.
| Level | What the agent does | What the human does | Typical time to reach |
|---|---|---|---|
| L0 · Shadow | Observes, scores and predicts; writes nothing | Runs the line as today; compares predictions to QC results | Weeks 1 to 6 of a design partnership |
| L1 · Assist | Recommends setpoint moves and explains why | Process engineer enters the move; QA reviews the log | After shadow-mode accuracy is shown |
| L2 · Supervised | Proposes a bounded move; it executes on approval | Approves each move in the review console | After assist-mode ROI is shown |
| L3 · Bounded | Moves low-risk compression, coating and inspection setpoints inside the approved design space | Handles exceptions; approves every release | After validation and change-control review |
There is no unattended level. Batch release and any move outside the approved design space always wait for a named QA reviewer, and every model or prompt change runs under GxP change control.
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
| Standard | Scope | Status |
|---|---|---|
| 21 CFR Part 11 | Audit trail, e-signatures, records | RUNNING Designed in from day one |
| GAMP 5 | Validation package for the platform | RUNNING In progress, planned for the first design partners |
| cGMP · ICH Q8 to Q13 | Design space, QbD, continuous manufacturing | RUNNING Design-aligned; site validation decides |
| SOC 2 Type I | Cloud control plane | QUEUED Planned, first six months |
| SOC 2 Type II | Cloud control plane | QUEUED Planned, months six to twelve |
| Data residency | Validated on-prem, air-gapped option | SUCCEEDED Available by design |
Failure modes
What happens when things go wrong
Stated plainly.
Caught by the design space and the reviewer
A wrong recommendation cannot leave the design space, and in assist or supervised mode a human sees it first.
Flagged, not acted on
Data-integrity risk flags hold the output and route it to the reviewer with the suspect record marked.
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.
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
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
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
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.
Integrations
It speaks tablet plant, not cloud
Pildeon reads and writes through the systems already on the floor: presses, coaters, granulators, PAT probes, MES, LIMS and historians. No rip-and-replace, no parallel historian. Connectors are built with design partners against their stacks; the vendors listed are the ones common in OSD plants.
GEA, Glatt, Syntegon, Freund-Vector, L.B. Bohle
Blender, granulator and fluid-bed telemetry; bounded setpoint writes
Fette Compacting, Korsch, IMA, Syntegon, MG2
Force curves, fill depth, ejection force; bounded setpoint writes
GEA, Glatt, O'Hara, Thomas, Freund
Spray, inlet, bed temperature, exhaust humidity; bounded writes
Bruker, Sartorius, Viavi probes and analysers
Spectra across blend, granulation, drying and compression
Line cameras and existing inspection systems
Frames, defect maps, reviewed images
Körber Werum PAS-X, Rockwell, Siemens Opcenter
Master batch records, campaigns, batch genealogy
Site LIMS, OSIsoft PI and equivalent historians
QC results, time-series backfill and replay
Azure AD, Okta, LDAP · NVIDIA Jetson and on-prem GPU
SSO, RBAC, named approvers · inference at the line
Enterprise
Standardise validated autonomy across the group
One policy model, one audit spine, one benchmark across every line in every plant, with formulation and tooling models kept private to each site.
- SSO, RBAC and named approvers down to the setpoint level
- Group autonomy policy with per-site override and approval chains
- Cross-plant benchmarking on yield, rejects, deviations and energy
- Validated on-prem and air-gapped plant-edge deployment options
- Validation packages and change-control records as first-class deliverables
- Dedicated deployment engineer and quarterly model review per site
The first meeting
What each stakeholder asks first
Pildeon has no customer quotes to show yet, and will not invent any. These are the questions the three buyers in an OSD plant ask in the first meeting, and the answer each gets.
"What does this do to my reject rate, my deviation count and my release time, and how will you prove it on my line rather than on a slide?"
Site or manufacturing director · economic buyerAnswer: shadow mode first, baseline captured before any autonomous action, a written ROI target before a paid pilot.
"Will it ever move a setpoint outside the design space we validated, and can I see why it recommended every move it made?"
Formulation or process engineer (MSAT) · championAnswer: no, by construction. Every recommendation carries its reasoning trace and its citation.
"Where is the Part 11 audit trail, who signs the release, and what happens to my validated state when the model changes?"
QA, quality and compliance lead · approverAnswer: an immutable audit spine, a named QA reviewer on every release, and every model change under GxP change control.
Get started
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