Pharmaceuticals solutions
Quality Control for Pharmaceuticals
If you're investing in quality control for pharma & biotech, you've probably already seen a few proofs-of-concept that never made it to production. Beryl Analytics specialises in the messy middle — turning prototypes into reliable systems your operators actually use.
Why pharmaceuticals teams choose Beryl Analytics for quality control
- Built for compounding value. Each quality control engagement leaves pharma & biotech with infrastructure that accelerates the next one — shared feature stores, reusable pipelines, documented data contracts.
- Real handover. We pair your team into the build from day one. By go-live, they own the system. We're optional from then on.
- Practical AI. We've shipped LLM-augmented analytics where they help, and stayed with simpler models where they outperform. Hype is not a strategy.
- Audit-friendly. Every model decision is traceable. Compliance and risk teams stop blocking — they start enabling.
- Track record. 1,000+ models in production. Across heavy-industry, regulated, and consumer domains.
How we deliver quality control engagements
- 01
Discovery sprint (week 1)
Two days on-site with your operators to map the workflow, half a day with leadership to align on the dollar metric, and an afternoon writing the scope memo we'll work to.
- 02
Spike the riskiest assumption (weeks 2-3)
Before committing to the build, we attack the assumption most likely to kill the project — usually data availability or operator adoption. A negative result here saves months.
- 03
Build, in public (weeks 4-8)
Daily commits to a shared repo your engineers can read. Weekly demo to the operator group. Nothing is built in private.
- 04
Production cutover (weeks 9-10)
A planned cutover with a rollback plan, monitoring, and a human in the loop for the first fortnight. We don't walk away from cold launches.
Frequently asked questions about Quality Control for Pharmaceuticals
How long does a typical Quality Control engagement take for a pharmaceuticals business?
Most quality control projects for pharma & biotech land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger pharmaceuticals programmes that touch multiple business units take 4-6 months end-to-end.
What data do you need to start a Quality Control project in pharmaceuticals?
Minimum viable inputs are 12-18 months of historical transactional or operational data, basic entity reference tables, and access to the systems that will consume the output. We can work with messy data — cleaning is part of the engagement.
Can Beryl Analytics integrate quality control with our existing pharma & biotech systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of pharmaceuticals-specific systems. Insights surface inside the tools your operators already use.
How do you measure success on a Quality Control engagement?
Before we model anything, we agree the business decision the output will change and the dollar metric we're targeting — revenue lifted, cost avoided, or risk reduced. Quality Control engagements in pharmaceuticals typically return 4-12x within the first year.
Do you work with pharmaceuticals businesses outside major NZ and AU cities?
Yes. We deliver remotely across New Zealand and Australia and visit on-site for discovery, key workshops, and go-live. Distance is not a blocker — many of our highest-impact quality control engagements have been with regional pharma & biotech.