Cosine MetricsAI Workflow Control Binder
This is a redacted, illustrative version of the exact artifact a Engagement produces — here for a fictional AI-assisted deviation-summary drafting workflow at a mid-size biologics manufacturer. It shows the structure, depth, and evidence you receive. Real binders are built for one of your workflows, with your systems and stakeholders.
Use-Case Charter & Context of Use
QA specialists use a hosted LLM to draft the narrative summary of a manufacturing deviation from structured event data. The draft is reviewed and approved by a qualified QA reviewer before entry into the eQMS.
Current-State Workflow Map
Event captured in eQMS-XX → specialist exports structured fields → drafts narrative (today: manual; proposed: AI-assisted) → QA reviewer edits/approves → entry to eQMS → CAPA evaluation. Six handoffs; two GxP-impacting steps identified.
Data-Integrity & Part 11 Flow Map
Inputs: deviation ID, dates, batch ID, process step, observation. No PHI. ALCOA+ assessed at each step; the AI draft is marked as draft, attributable to the specialist, never an original record until QA-approved in the validated eQMS.
| ALCOA+ attribute | Exposure | Control |
|---|---|---|
| Attributable | AI draft authored outside eQMS | Draft tagged to specialist; approval recorded in eQMS audit trail |
| Original / Accurate | Hallucinated detail risk | Mandatory line-by-line QA review against source fields; correction log |
| Contemporaneous | Draft/approval time gap | Timestamped on eQMS entry, not on AI draft |
Risk & Impact Assessment (GAMP 5)
Classified high decision-impact: output enters a GxP record affecting batch disposition context. Top risks: fabricated specifics, omission of a material fact, over-reliance by reviewers, and silent model change. Each mapped to a control in Parts 5–9.
Human-Review & Accountability Model
Specialist drafts; a qualified QA reviewer (defined competency) verifies every statement against source data and approves; QA manager accountable for the workflow. Nothing auto-accepted. Corrections logged and trended as a quality signal.
Validation / Acceptance-Test Plan (CSA-aligned)
Risk-based test set of representative and adversarial deviations; acceptance criteria on factual fidelity, completeness, and absence of fabricated content; an error taxonomy (omission, fabrication, mischaracterization); before/after review-time and error-rate metrics.
AI Tool & Vendor Assessment
Reviewed: data handling, retention, whether inputs train the vendor model (must be off), access controls, audit logging, SOC 2 posture, and the model-change/version-pinning policy. Security review led by a Security+-certified reviewer.
SOP Pack
Workflow SOP · human-review SOP · data-handling SOP · exception/escalation SOP · tool-change review SOP · quality-sampling SOP. (Full SOP text redacted in this sample.)
Monitoring & Quality-Sampling Plan
Defined sampling rate of approved summaries, error categories trended monthly, KPI dashboard (error rate, review time, correction types), and explicit revalidation triggers (model version change, error-rate threshold breach, workflow change).
Training Deck & Sign-off
Role-based rules, side-by-side acceptable/unsafe examples, a competency check, and a training-record template for specialists and reviewers.
Executive Go/No-Go Memo
Readiness: conditional go — pilot on low-severity deviations with the controls above. Residual risk: moderate, mitigated by mandatory review and monitoring. 30/60/90: pilot → measure error/time → expand or hold based on monitoring data.
This is what "inspection-ready" looks like
For one of your workflows, with your systems and stakeholders — fixed fee, 6–12 weeks.