Eliminate fragmentation and reduce IT risk across clinical sample data systems

Trusted by multiple top pharmas, QuartzBio Sample Intelligence closes the clinical sample data governance gap in your R&D architecture, empowering IT teams with a platform that’s harmonized, vendor-agnostic, and built for enterprise scale.

Fragmented sample data creates hidden enterprise risk.

IT teams at global pharmas manage sophisticated enterprise data architectures, but sample data remains a persistent gap.

When you’re grappling with as many as 20+ vendors, systems, and global partners,

  • Data inconsistencies propagate across downstream systems
  • Audit readiness becomes reactive, not guaranteed
  • Integration dependencies become brittle and costly to maintain
  • Critical sample issues are discovered too late—after downstream impact

Over time, IT inherits a growing burden of maintaining pipelines, scripts, and point-to-point integrations instead of enabling innovation.

What R&D IT teams gain with Sample Intelligence:

  • Standardize across your entire ecosystem
    Automated, vendor-agnostic ingestion ensures all sample data conforms to governed enterprise formats.
  • Reduce compliance and audit risk
    Built-in GxP-aligned governance, audit trails, and traceability across all sample data.
  • Stabilize your data architecture
    Replace brittle point integrations with a scalable, centralized data layer.
  • Scale without re-architecting
    Onboard new vendors and systems without introducing new integration risk or complexity.

If you’re ready to fix data architecture problems at their source and regain freedom to innovate and scale, explore Sample Intelligence.

Case Study: How a top 5 pharma doubled trial capacity and eliminated hidden data loss

“With QuartzBio, we spend 85% less time on sample-related tasks.”
–Top Biopharma

A top 5 pharma discovered that outdated sample ops workflows were putting their pipeline at risk. With QuartzBio, they eliminated fragmented systems and gained full visibility into sample data—accelerating timelines and improving dataset quality.

  • Standardized APIs and formats across all vendors — no more custom transformation pipelines
  • 90% reduction in data wrangling effort across the portfolio – reducing operational risk
  • 98% reduction in manual data reconciliation effort – improving data integrity
  • Stronger data governance and interoperability across the enterprise R&D data stack
  • Ability to support scaling portfolios without continuously re-architecting systems

Harmonize sample data without adding complexity.

Learn how your team can close the governance gap, stop spending time on one-off integrations, and build architecture that scales. Request a 15-minute workflow review.

Identify the biggest data architecture risks in your sample ecosystem: