Portfolio Accelerator · Pharma Manufacturing · Google Cloud
Manufacturing Quality AI on Google Cloud for Pharma: An Implementation Blueprint
A documented GCP architecture for deviation triage, CAPA prediction, and OOS root-cause assist, built for GxP-regulated manufacturing quality teams.
problem
Why Pharma Manufacturing Quality Is the Hardest Place to Deploy AI
Deviation management, CAPA tracking, batch release, and out-of-specification investigations sit at the center of a pharma quality system, and every one of those workflows is subject to GxP, 21 CFR Part 11, and Annex 11 audit scrutiny. A generic MLOps platform bolted onto a quality system usually fails that audit because it was never built with ALCOA+ data-integrity principles or a validated change-control process in mind.
demo
Inside the Accelerator: What We're Building on Google Cloud
This page shows the architecture for Kriv AI's manufacturing quality accelerator on GCP. In the interest of transparency: this is the earliest-stage accelerator in our portfolio right now. The environment setup is in progress and the reference-architecture review is complete; the data pipeline, models, and dashboards are specified but not yet built. We're presenting this honestly as a blueprint.
Architecture: BigQuery, Vertex AI, and an Audit-Ready Governance Layer
The design calls for BigQuery as the batch and quality-event data warehouse, a Pub/Sub and Dataflow pipeline for real-time sensor data, Document AI for OCR on scanned batch records, Vertex AI hosting the deviation, CAPA, and out-of-specification classification models, and Looker Studio for quality dashboards, all governed by Cloud Audit Logs with a 7-year retention sink to support GxP recordkeeping.
Synthetic Data, Real Infrastructure: How We're Building This Without Touching Your Batch Records
The plan is for the demonstration to run entirely on synthetic batch and sensor data, so the architecture can be inspected and discussed without any client batch record ever touching the environment. Design targets include 90%+ Document AI extraction accuracy on scanned batch records and an excursion-predictor model targeting an AUC above 0.80, both design goals, not measured results yet.
status
What's Real Today, and What's Roadmap
Honestly: this accelerator is at an early environment-setup stage, with 29 reference repositories reviewed across BigQuery, Vertex AI, Document AI, Dataflow, and GCP infrastructure-as-code. The data warehouse, real-time pipeline, models, and dashboards described above are designed and specified but not yet built and exercised on synthetic data. This is the least mature accelerator in our current portfolio, and we'd rather say so than dress it up.
outcomes
What This Is Designed to Solve: Deviation Triage, CAPA Prediction, and OOS Root-Cause Assist
The architecture targets three concrete workflows: routing incoming deviations to the right investigator with a suggested severity classification, flagging which open CAPAs are likely to recur based on historical patterns, and assisting root-cause investigation on out-of-specification and out-of-trend results by surfacing related batch and sensor data automatically.
engagement
GCP Pharma Manufacturing Quality AI Consulting: How We Engage
From This Accelerator to Your Validated Production System
A scoped engagement builds this design out against your real GCP environment and quality system data, with validation, change control, and GAMP 5 documentation carried through from day one rather than bolted on afterward.
differentiation
Why a Boutique Governed-AI Firm, Not the Big 4, for This Build
A large consulting firm brings a digital-transformation slide deck to a manufacturing quality conversation. Kriv AI brings a named GCP architecture, mapped to the exact regulatory frameworks a QA and manufacturing-ops team already lives by, Part 11, Annex 11, and GAMP 5, and we're upfront when a piece of that architecture is still on the roadmap rather than built.
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Straight answers
Frequently asked questions about Manufacturing Quality AI on Google Cloud for Pharma: An Implementation Blueprint
Is the manufacturing quality AI accelerator built and running on GCP today?
No, and we want to be direct about it. This is the earliest-stage accelerator in our current portfolio: the environment setup is in progress and the reference architecture is specified, but the data pipeline, models, and dashboards are not yet built.
What GCP services does the architecture use?
BigQuery for the batch and quality-event data warehouse, Pub/Sub and Dataflow for real-time sensor data, Document AI for scanned batch record OCR, Vertex AI for classification models, and Looker Studio for dashboards.
Will this use real batch or quality records?
No. This accelerator, once built, will run entirely on synthetic batch and sensor data. No real client batch records are used at any stage of the demonstration.
What compliance frameworks is this designed around?
21 CFR Part 11, EU Annex 11, GAMP 5, and ALCOA+ data-integrity principles, the specific frameworks a pharma manufacturing quality system is audited against.
What is this accelerator designed to solve?
Deviation triage and severity classification, CAPA recurrence prediction, and root-cause assistance for out-of-specification and out-of-trend investigations.
Can Kriv AI build this out for our manufacturing site's GCP environment?
Yes. A scoped engagement can build this architecture against your real GCP environment and quality system, with validation and GAMP 5 documentation built in from day one. Contact us to discuss scope and timeline.
Ready to see the accelerator run against your data model?
Bring your requirements to a working session and we'll walk through the live system.
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