Your clinical data meets regulatory standards. Your analytics infrastructure does not.

Pharmaceutical companies generate data across every stage of the product lifecycle: research and development, clinical trials, manufacturing, quality control, regulatory affairs, and commercial distribution. Each stage runs on specialized systems designed for compliance and operational control, not for cross-functional analytics.

The result is an organization that can meet regulatory requirements for any individual system but cannot answer cross-domain questions without weeks of manual data assembly. How does batch quality correlate with raw material supplier? Which manufacturing parameters predict analytical results? What is the true cost of producing and distributing a specific product? The data to answer these questions exists. The infrastructure to connect it does not.

CorrDyn spent over a decade building data infrastructure for life sciences manufacturers. The problems we solve — IoT sensor pipelines, LIMS integration, quality analytics, ML on manufacturing data — are the same problems pharmaceutical companies face at every stage of growth, from clinical development through commercial manufacturing.

Manufacturing data infrastructure. IoT sensor pipelines that ingest data from production equipment at sub-second intervals, batch record data pipelines, and quality analytics dashboards. These systems provide real-time visibility into machine performance, quality metrics, and failure patterns across the manufacturing floor, from API synthesis through final dosage form production.

Research and lab data integration. Pipelines that connect LIMS (laboratory information management system) outputs, analytical instrument data, and research databases to a unified analytical layer. Scientists and process engineers query cross-system data directly instead of waiting for IT to run custom reports. Technology transfer teams get the manufacturing-to-research feedback loop they need to iterate on processes.

Compliance-aware analytics architecture. Data pipelines with full lineage tracking, audit trails, and access controls appropriate for GxP (Good Practice) environments. The analytical layer sits alongside validated source systems without changing their validation status. Quality teams get trend data for process improvement. Regulatory affairs gets submission-ready data. The compliance framework stays intact.

Clinical and commercial data connectivity. Pharmaceutical data challenges extend beyond the manufacturing floor. Clinical trial data, commercial sales and distribution records, and regulatory submission artifacts all live in separate systems. We build the integration layer that lets cross-functional teams answer questions that span research, manufacturing, and commercial operations.

ML readiness and model development. For pharmaceutical companies exploring predictive quality, process optimization, or analytical method automation, we build the data preparation layer first: clean data, governed models, feature engineering pipelines, and labeled training sets. The model is the last step. The foundation is what makes it work. For a look at how physics-based simulation and ML are converging in drug discovery, listen to our conversation with Schrödinger's Robert Abel on [computational drug discovery](/podcasts/data-in-biotech/ep-68-robert-abel-schrodinger-drug-discovery).

Our life sciences experience is not theoretical. We spent over a decade inside life sciences manufacturing, building and operating the data systems that connect sensors to dashboards to ML models. We understand the organizational dynamics of pharmaceutical companies: the separation between IT and science, the compliance requirements that constrain data access, the skepticism of lab teams toward tools that have not been proven on their data. We start with a working prototype on real data, validated against numbers your team already trusts.