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Annual Product Quality Review
APQR · Automated Compilation
From one month of manual work to one day with AI — inside your environment, on your systems.
How CGLabs delivers APQR to three illustrative customers, compiling regulatory reports across TrackWise, JMP, ERP, and SharePoint without any data leaving their network.
CGLabs · CLAIRE PlatformLeadership Brief · 2026
The Problem
02 / 12

Every APQR is a month of manual work across four or more systems — for one product.

Typical effort per product~1 month of manual compilation + review
Systems involved4+ per APQR (TrackWise + JMP + ERP + SharePoint is the baseline)
Annual cost per product$50–150K in loaded labor for one report
Scale problemA large pharma with 20 products spends 20+ months of FTE per year on APQRs alone
Security stanceNon-negotiable: product data cannot leave their network, cannot use public AI
The Problem
03 / 12

Every existing option fails them.

Manual compilation

QA writers pull data from four systems, clean it, reconcile it, compile it. Slow, error-prone, impossible to scale as product count grows.

Big consultancies

Will do it for you at $200–400K per product. No leverage, no IP retained, same manual effort hidden behind a bill.

Generic ETL tools

Informatica, Talend. Can pull data but don't understand GxP, can't reason across quality events, don't produce regulatory documents.

DIY scripts

Home-grown Python scripts. Brittle, unauditable, tribal knowledge. Regulators don't accept them as defensible compilation.

No vendor today ships an appliance that runs inside the customer environment, connects to the four systems every pharma already uses, and compiles a defensible APQR in one day. That gap is what APQR fills.
The Value
04 / 12

The math is obvious.

~1 month
Manual effort per APQR today
→
1–2 days
With APQR appliance (including QA review)
90% time reduction per product. For a pharma with 20 products, that's ~18 months of FTE saved per year — at ~$200K loaded cost, $3.6M in annual labor freed. And every APQR is now defensible on its evidence pack.
Illustrative Scenarios
05 / 12

Three illustrative customers — different system stacks, different product counts, different AI environments.

Customer A
Large pharma · hypothetical
Systems
TrackWise + JMP + SAP ERP + SharePoint
Products in scope
20 APQRs/year
Hard part
20+ FTE-months annually; can't keep up with product launches
Current cost
~$3M / year in labor for APQRs alone
AI environment
Their Azure OpenAI tenant
Customer B
Mid-size biotech · hypothetical
Systems
MasterControl + LabWare LIMS + SAP
Products in scope
5 APQRs/year
Hard part
Needs defensible compilation; regulator asked for full traceability
Current cost
~$500K / year in labor + consultant fees
AI environment
Their AWS Bedrock
Customer C
Specialty generics · hypothetical
Systems
TrackWise + JMP + ERP + file shares
Products in scope
40+ APQRs/year
Hard part
Product count grew faster than team; quality backlog mounting
Current cost
Can't afford to do all of them manually
AI environment
Self-hosted (no cloud AI)
Illustrative Scenarios
06 / 12
Three different system stacks. Three different product counts. Three different AI environments.
Looks like three projects.
It is one product, configured three ways.
Customer Concerns & Answers
07 / 12

Six questions every regulated buyer asks — and our structural answers.

Where does our data go?Nowhere. APQR installs as a Docker appliance inside the customer's data center. Product data, batch records, and narratives never leave their network.
Who has access?Only their own staff. Our SE configures alongside them on their network; we never need to see their data.
What does the AI see?Only their LLM environment. Azure OpenAI, AWS Bedrock, or self-hosted — whatever they already approved.
Can we prove what happened?Yes — for every data point. Every extraction, every transformation, every AI decision logged in the APQR's evidence pack.
What if you go away?Nothing breaks. The appliance runs independently. Historical APQRs remain theirs forever.
How do we control access?Their identity provider. Azure AD / Okta via SAML. Same offboarding as their other quality systems.
How APQR Works
08 / 12

Six specialized AI agents, one orchestrated flow, one regulatory document.

Each agent handles one section of the APQR. The flow orchestrates them automatically with a human review gate before finalization.

AGENT 01
Data Ingestion
Pulls from TrackWise, JMP, ERP, SharePoint. Handles Excel, scanned PDFs with OCR, REST APIs. Loads into knowledge graph.
AGENT 02
Data Quality
Auto-fixes text vs numeric issues, character truncations, date formats. Reconciles duplicates across reports.
AGENT 03
Batch & Materials
Summarizes batches manufactured, raw materials, components, packaging. Cross-references ERP with batch records.
AGENT 04
Quality Events
Analyzes deviations, investigations, CAPAs, change controls. Identifies trends and open items.
AGENT 05
Statistical
Processes stability data, analytical results, CPPs, in-process controls, yield. Generates trend summaries from JMP.
AGENT 06
APQR Compiler
Assembles all sections into the final report. Formats per regulatory template. Generates Word/PDF output.
Delivery Approach
09 / 12

Every customer engagement follows the same shape. The differences are configuration, not code.

01
Install
Docker stack in their data center, SAML wired
02
Connect
Configure source system connectors + credentials
03
Pilot
Compile one product end-to-end as proof
04
Refine
QA reviews, mapping tightened, format validated
05
Scale
Run across all products; human review gate per APQR
06
Hand off
Customer QA team runs compilations independently

Same appliance. Same SE playbook. What changes per customer is which source systems are connected and how fields are mapped — all configuration, not code.

Cost & Pricing
10 / 12

Customer-side cost is trivial. Pricing scales with their product catalog.

Customer Infrastructure
Under $1K / month to host.
Existing on-premiseThey already have host capacity~$0
Customer Azure / AWSOne VM + 500 GB SSD$500–700/mo
Our-side hostingAPQR runs in their environment, not ours$0
Pricing Model
Annual subscription — per product.
Platform license (annual)Right to run the APQR appliance$150–250K
Per-product subscriptionOne annual report, unlimited re-runs$40–75K / product
Implementation servicesConnectors, mapping, pilot, scale$200–400K

A Customer-A-style pharma with 20 products pays ~$950K/year on the platform to replace ~$3M in manual labor. Numbers are placeholders — sales/finance to validate.

Deliverables
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What the customer gets for every product, every year.

1
The formal APQR documentWord / PDF, per regulatory template (21 CFR 211.180(e), EU GMP Annex 20). Ready for signature and submission.
2
Per-data-point evidence packEvery figure in the report traced to source system + query + transformation + AI reasoning — defensible to any regulator.
3
Knowledge graph view of the productAll batches, deviations, CAPAs, materials linked together. Queryable for the future without redoing the extraction.
4
Reusable pipeline for subsequent yearsYear 2 runs in hours, not days. Year 3 is effectively free operational cost. The catalog of configured products compounds.
5
No ongoing dependency on usOnce the appliance is configured, their QA team runs APQRs independently. We remain available for new source systems and support.
Operating Principle
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Across all three customers, the working pattern is the same:
Their installation. Their network. Their product data. Their AI. Their identity. Their people sign off. Their evidence to keep.
That sentence is what makes APQR acceptable to a regulated buyer. It is not a feature list — it is the operating model. Everything else is built on top of that foundation.