G‑14 regulated action review

Regulated AI needs admissible action.

Choose a regulated workflow and see the action boundary, authority check, proof receipt, evidence matrix, API entry point, and qualified review path before AI touches consequence.

Open the review consoleRequest deployment review
AI proposalBefore consequence
01Base proposal

Model, agent, workflow, tool, or machine proposes a consequential action.

02G‑14 external decision

Boundary, authority, and runtime evidence are checked before release.

PreserveCorrectHoldBlock
03Regulated action channel

Record, claim, batch step, robot action, customer promise, or governed workflow.

Deployment proofProposal + boundary + authority + evidence + outcome when configured
Builder briefsAI-readable MarkdownStructured route packQualified intake

Why this matters

Policies describe intent. Runtime control decides whether action proceeds.

Regulated AI buyers are not only asking whether a model is accurate. They are asking what the system can touch, where permission is checked, who can admit the action, what happens when evidence is missing, and what proof survives after the action is challenged.

Control AI before consequence

Put a governed runtime boundary between model proposals and actions touching records, money, claims, patients, machines, public services, or institutional commitments.

Start without replacing the stack

Begin in replay or shadow mode beside one workflow, then move toward assist or control only when authority, evidence, and safety boundaries are explicit.

Give auditors evidence

Export proof receipts showing what was proposed, what was allowed or changed, what evidence was used, who reviewed holds, and what happened after execution.

Serve builders and consultants

Give systems architects, integrators, AI governance advisors, and fractional CTOs a concrete path for adding external control without building a proof layer from scratch.

The governed action corridor

One path from proposal to proof.

The same governed pattern applies across regulated environments: proposal, boundary, authority, decision, and proof.

  1. 01Proposal

    AI asks to act

    An agent, model, robot, workflow, or tool proposes an action with possible institutional consequence.

  2. 02Boundary

    Consequence is identified

    The runtime determines whether the proposal touches records, money, patients, machines, release, public authority, or regulated commitments.

  3. 03Authority

    Admission is checked

    Configured role, policy, evidence, risk, timing, and escalation rules determine whether the action can proceed.

  4. 04Decision

    G‑14 controls release

    The action is preserved, corrected, held, blocked, or released according to the deployment mode and evidence available.

  5. 05Proof

    The decision survives

    A proof receipt records what was proposed, what was allowed or changed, what evidence was used, and what happened after execution.

Regulated AI action review

Describe the action. See the G‑14 control path.

Enter one action and G‑14 maps the boundary, authority, decision posture, runtime evidence, and signed assessment that would start a real deployment review.

GxP action control

Pharmaceuticals & Life Sciences

Govern AI-assisted lab, quality, manufacturing, and regulated-document actions before they become record, release, or patient-impacting consequence.
Request this review

G‑14 returns a signed scenario assessment. Customer deployment proof receipts require a configured runtime action path.

Selected action pathBatch-record update or review step

AI proposes: Batch-record update or review step.

Current modeLive observation

Live proposals are evaluated beside the workflow without intervening.

Deployment mode
Active configurationPharmaceuticals & Life Sciences / Shadow

Side-by-side proof packet showing what G‑14 would have preserved, held, corrected, or blocked.

Boundary
Pharmaceuticals & Life Sciences boundary: Which AI actions can touch GxP records, quality events, samples, release decisions, or regulated documents?
Authority
Admission authority starts with Quality, validation, lab automation, manufacturing, and regulated-document owners.
Latency posture
Online evaluation path. Policy-only gates target sub-ms to low-ms locally; evidence retrieval is deployment-dependent.
Next step
Compare live workflow behavior against G‑14 decisions.
  1. 01

    AI proposes: Batch-record update or review step.

  2. 02

    Pharmaceuticals & Life Sciences boundary: Which AI actions can touch GxP records, quality events, samples, release decisions, or regulated documents?

  3. 03

    Shadow mode: Live proposals are evaluated beside the workflow without intervening.

  4. 04

    Proof output: Side-by-side proof packet showing what G‑14 would have preserved, held, corrected, or blocked.

Signed scenario assessmentG14-PHARMA-BATCH_RECORD_UPDATE_O
Proposal
AI proposes: Batch-record update or review step.
Boundary
Pharmaceuticals & Life Sciences boundary: Which AI actions can touch GxP records, quality events, samples, release decisions, or regulated documents?
Authority
Admission authority starts with Quality, validation, lab automation, manufacturing, and regulated-document owners.
Decision
Observed beside live workflow
Evidence
system_record, operator_role, sop_version, workflow_state
Timing
Online evaluation path. Policy-only gates target sub-ms to low-ms locally; evidence retrieval is deployment-dependent.
Risk
If released without control, this action can create pharmaceuticals & life sciences consequence without a reviewable admission record.
Outcome
Side-by-side proof packet showing what G‑14 would have preserved, held, corrected, or blocked.
Waiting for signed assessment

Live observation. Receipt hash-bound where configured.

API / Test Lab preview/api/regulated/actions/evaluate
Request
{
  "scenario": "Batch-record update or review step",
  "industry": "pharma",
  "workflow": "Batch-record update or review step",
  "deployment_mode": "shadow"
}
Decision response
{
  "status": "pending",
  "path": "/api/regulated/actions/evaluate"
}
Control anchorBuyer questionG‑14 evidence
21 CFR Part 211 / CGMP procedural evidenceCan we show who admitted AI-assisted SOP, training, CAPA, validation, or change-control evidence before it became part of the quality record?Procedural action receipt with proposal, boundary, authority route, evidence references, decision, and final outcome.
21 CFR Part 11 / EU Annex 11Can we prove who admitted a record-affecting action, when, and from what evidence?Proof receipt with proposal, authority, timestamp, decision, evidence references, and audit binding where configured.
GAMP 5 / CSVCan validation teams review one workflow before live enforcement?Replay or shadow packet showing expected action path, hold behavior, release rules, and verifier output.
Quality system reviewCan a held or blocked AI action be routed to the right quality owner?Authority context, reviewer custody, hold resolution, risk event, and final outcome.
FDA AI/drug development review supportCan the organization explain how AI-assisted actions were controlled in a regulated process?Boundary map, evidence matrix, proof packet exports, and deployment-mode history.
What architects need from the client
  • One regulated workflow and system of record
  • Authority model for quality, validation, and operations
  • Evidence sources available at runtime
  • Current QMS/CSV validation boundary
What the review can produce
  • Action-boundary map for leadership
  • Proof-packet field list for quality review
  • Replay or shadow deployment recommendation
  • SDK/API access request with workflow context
Boundary honesty

G‑14 does not replace a QMS, CSV program, validation owner, clinical judgment, regulatory submission duty, or required human release authority.

Integration start

Start with batch-record update or review step and send one governed action packet through G‑14 before widening scope.

POST /v1/regulated/actions/evaluate

Pharmaceuticals & life sciences

The procedural layer is the GxP system.

Pharma quality teams often agree at the obvious edges: batch record review is critical, scheduling is usually not. The risk lives in the middle. If AI authors an SOP, training record, CAPA effectiveness review, validation protocol, or change-control rationale, it is shaping the evidence trail an inspector may later read.

External control belongs before that procedural evidence changes: what was proposed, who admitted it, what evidence was used, and what proof survives.

Read the GxP briefRequest workflow review
SOP authoring and controlled-procedure changesTraining material generation and role-specific instructionCAPA effectiveness review and quality-event evidenceValidation protocol drafting and acceptance criteriaChange-control documentation and impact assessmentBatch-record review and manufacturing workflow evidence

Deployment path

Move from proof to control without pretending every workflow is ready for enforcement on day one.

Regulated customers can start with evidence before live control. The right first step depends on the action, authority model, evidence sources, and operational risk.

Replay

Run G‑14 against historical or synthetic action records to expose authority gaps, missing evidence, and proof requirements.

Shadow

Evaluate live proposals without intervening, producing a side-by-side record for governance, security, and operations.

Assist

Recommend hold, correction, block, or release decisions to a human or host workflow with evidence attached.

Control

Enforce the release decision before action reaches the tool, robot, workflow, or downstream system.

API and SDK path

Build the control layer at the moment of action.

Builders connect G‑14 where the proposed action leaves the model, agent, workflow, robot, or tool and asks to touch a regulated environment.

Explore the SDKRequest access
01

Action packet

Your agent, tool, robot, workflow, or orchestration layer sends the proposed action, context, authority model, available evidence, and release deadline.

02

External decision

G‑14 returns preserve, correct, hold, block, or release with reasons, custody, and configured proof-packet references.

03

Verifier output

Security, compliance, audit, or customer assurance teams can review exported evidence and verify the governed decision path outside the runtime.

Assurance posture

Evidence mapping for serious regulated review.

These references describe buyer-facing readiness paths and control mappings, not completed certifications or legal compliance claims unless stated in a signed customer, assessor, or regulator-facing document.

FDA AI in drug and biological product development guidance support21 CFR Part 11 and EU Annex 11 evidence mappingHIPAA Security Rule audit and risk-analysis supportNIST AI RMF and NIST CSF 2.0 control mappingSR 11-7 model-risk governance supportSEC Regulation SCI incident and control review support where applicableEU AI Act high-risk system evidence orientationISO/IEC 42001, ISO/IEC 27001, SOC 2, CMMC, and FedRAMP readiness paths where scoped

Bring one regulated action path.

G‑14 will help map the proposal, boundary, authority, evidence, deployment mode, and proof packet needed before AI action reaches consequence.

Request regulated deployment review