Pildeon has no measured customer results yet, so these are scenario studies: how each wedge workflow would run under Pildeon, which agents and products it uses, what gets measured, and what stays with a human. No plant names, no invented percentages.
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.
Each follows the same shape: the pain, the wedge, the agents, what is measured, what the human keeps.
Pain: capping and weight variation found at inspection or in QC. Wedge: Compress-and-Fill holds force and feed-frame speed inside the design space while Visqa inspects every tablet. Measured: reject rate, capping incidence, weight and hardness variability against the shadow baseline. Human keeps: every out-of-space move and release.
Pain: blend uniformity confirmed hours later in the lab. Wedge: Spectryn scores in-line NIR and calls the end point on evidence with confidence bounds. Measured: lab versus in-line agreement, blend time, content-uniformity rejects, release time. Human keeps: method validation and the release signature.
Pain: twinning, mottling and over- or under-coat causing rework. Wedge: Coat-and-Dry re-phases spray and inlet temperature inside the window; Visqa watches appearance. Measured: coating defect rate, rework, drying energy per batch. Human keeps: recipe changes and release.
Pain: slow, expensive validation runs because nobody can simulate the as-made batch. Wedge: Twindra fits a formulation- and tooling-specific twin and proposes a design-space envelope. Measured: predicted versus measured quality attributes on the first validation batches. Human keeps: the design space, the validation protocol, the sign-off.
The reference run on this site is Scenario 1 extended through coating and release: compression inside the design space, Visqa inspecting, Spectryn's evidence chain, QA approval, Conforma's genealogy.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Plan, thought, action, observation, exactly as the orchestrator records it.
Run one immediate-release tablet batch through granulation, compression, coating and inspection inside the approved design space, with QA approval before release.
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.
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.
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.
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.
Execute candidate #14 under autonomy level L2: five setpoint moves inside the design space, each logged. Batch release routed to QA.
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.
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.
These are the MVP success metrics and the measurement rules a design partner agrees to before shadow mode starts.
Pildeon is pre-launch. These are the latency and scale targets the platform is engineered against. None is a shipped result, and each will be measured against a design partner's own QC data.
Training corpus targets across design partners: 10M+ tablet and coating images, 50k+ NIR and PAT spectra, 100k+ process time-series windows, 5k+ labelled deviation investigations. Targets, not holdings.
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.
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.
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.
| 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 |
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.
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.
Pick the scenario closest to your pain. Shadow mode turns it from a scenario into a baseline.
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