PharmTech.AI

AI Validation Rationale

A risk-based, CSA "validation-by-design" rationale for the Mock-FDA-Inspection rehearsal and its Evidence Pack — the context of use, the assurance it warrants, and the controls that keep it from becoming a compliance liability.

Demonstration sample · draft for qualified review
1

System & context of use

PharmTech.AI Mock-FDA-Inspection is an AI-assisted training and rehearsal simulation. A cohort of pharma personnel responds to simulated inspection stations; the system scores their decisions against operator-authored, SME-reviewed ideal answers and produces a Readiness Evidence Pack (a coaching debrief). Intended users: site quality, manufacturing, and L&D teams preparing for a scheduled PAI, a routine GMP inspection, or post-483 readiness.

GxP process touchedPersonnel readiness / training preparation — adjacent to, but not part of, any record-generating GxP system.
Records createdA training/rehearsal coaching record (the Evidence Pack). It is not a 21 CFR 211.25 training/qualification record and is not filed as a GxP record by the system.
Decision authorityEvery consequential judgment (qualification, release, disposition, CAPA) remains with the customer's own personnel and QMS. The system advises and rehearses; it never decides.
2

Context-of-use boundary

What it does
  • Rehearses inspection decisions under realistic prompts
  • Scores decisions vs. SME-reviewed ideal answers
  • Produces a reviewable coaching Evidence Pack
  • Exports a per-learner training record (SCORM/xAPI)
What it does NOT do
  • Write to or alter any GxP record
  • Make release, disposition, or qualification decisions
  • Draft fileable CAPAs, deviations, validation rationales, or 483 responses
  • Replace your gap assessment, audit, or QMS
3

Risk assessment & assurance tier

Risk dimensionImpact of an AI errorRating
Patient safetyIndirect at most — the system informs training only; it does not touch product, process, or release.Low
Product qualityIndirect — no batch, in-process, or disposition decision is taken by the system.Low
Data integrityOutputs are training records, version-controlled and attributable; they do not enter the validated GxP data flow.Low

Assurance tier (CSA, risk-based): Low — leveraged / unscripted + targeted scripted testing. Because the system makes no GxP decision and writes to no GxP record, assurance effort is focused on the two things that carry real consequence: content validity (citations and ideal answers) and reproducibility — not on exhaustive scripted testing of a record-keeping system it is not.

4

Human-in-the-loop design

  • Authoring: scenarios, ideal answers, and scoring rubrics are operator-authored and SME-reviewed before release.
  • Content gate: every CFR/ICH/USP citation is retrieval-grounded against a controlled source corpus and SME-verified — not model recall — and date-stamped to current regulation.
  • In-session: a facilitator runs the rehearsal; the customer's personnel own all interpretation and any downstream action.
  • Override: any human correction to a model output is captured with attribution and timestamp.
5

Acceptance criteria

CriterionAcceptance
Content validity100% of citations and ideal answers in a released scenario are SME-approved and version-pinned.
ReproducibilityIdentical inputs produce a scored Evidence Pack within a defined scoring tolerance — and the same readiness band and per-theme pass/practice classification — across re-runs (pinned model + prompt + scenario versions), verified against golden cases. Not a claim of bit-level determinism.
Boundary visibilityThe CSA context-of-use statement renders on every Evidence Pack.
CompletenessEvery theme returns a station, decision, ideal answer, citation, rationale, and score.
AttributionInputs, outputs, and human overrides are captured with user + timestamp.
6

ALCOA+ & audit trail

Training records are Attributable (named/role + timestamp), Legible, Contemporaneous (captured at run close), Original and Accurate to the run, with completeness, consistency, enduring storage, and availability for the customer's review. Scenario/scoring content is version-controlled with provenance printed on the artifact.

7

Change & model-drift control

  • Versioning: scenarios, rubrics, and the citation corpus are change-controlled; releases are dated and logged.
  • Model pinning: model and prompt versions are pinned; a model change triggers re-qualification of affected scenarios.
  • Regulatory currency: citations are reviewed on a defined cadence and on any regulation/guidance change.
  • Drift check: periodic re-scoring of golden cases confirms scoring stability over time.

Review & approval

Prepared / reviewed by (PharmTech.AI)
Name / credential · date
Accepted for context of use by (customer Quality)
Name / title · date

Validation Rationale · template v0.3 · 2026-06-23 · aligned to CSA / GAMP 5 (2nd ed.) risk-based principles

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Demonstration sample. Illustrative validation-by-design rationale for evaluation; not a completed validation deliverable. Content is illustrative and pending SME and customer sign-off.

PharmTech.AI is an independent service of Aligned Executive Solutions, Inc. and is not affiliated with, endorsed by, or sponsored by the U.S. Food and Drug Administration. © 2026 Aligned Executive Solutions, Inc. · PharmTech.AI · info@pharmtech.ai