Your machines generate terabytes. Your decisions still rely on spreadsheets.

Biotech manufacturers sit on some of the richest operational data in any industry. Every production run generates sensor telemetry, environmental readings, quality measurements, and machine performance logs. The gap between data generated and data used is enormous.

The pattern is consistent across the industry: machines generate terabytes, but quality decisions still depend on spreadsheets. Batch traceability takes hours of manual assembly. Data science teams wait months for clean training data that never arrives. The infrastructure between raw sensor output and business insight does not exist.

CorrDyn has spent over a decade building exactly that infrastructure for biotech manufacturers, including 29+ statements of work at a single Fortune 500 life sciences company. The problems we solved there are the same problems biotech manufacturers face at every stage of growth.

Real-time machine monitoring. IoT data pipelines that ingest sensor data at sub-second intervals, store it in time series databases, and surface it in dashboards that manufacturing engineers actually use. Whether you have 10 machines or 100, the architecture scales without rearchitecting.

Quality analytics and traceability. Robotic arm performance analysis with error correlation, vision system analytics, and batch-level data pipelines that connect raw machine output to quality outcomes. When a failure pattern emerges, your team sees it in minutes instead of discovering it during a post-mortem days later.

ML infrastructure and model deployment. Most biotech companies want predictive maintenance or image-based defect detection but lack the data foundation to support it. We build the data preparation pipelines, feature engineering layers, and labeled training sets that make machine learning viable. The model is the last step, not the first.

Hear from biotech leaders on Data in Biotech: [3D printing therapeutics at scale with Aprecia](/podcasts/data-in-biotech/ep-66-aprecia-3d-printing-therapeutics), [separating AI hype from reality](/podcasts/data-in-biotech/ep-67-ben-locwin-ai-hype-biotech), and [computational drug discovery at Schrödinger](/podcasts/data-in-biotech/ep-68-robert-abel-schrodinger-drug-discovery).

Data platform assessments and roadmaps. For companies earlier in their data journey, we deliver a structured assessment that maps your current state across manufacturing, quality, and data science functions, identifies the gaps between where you are and where you need to be, and prioritizes what to build first.

This is CorrDyn's deepest vertical. The combination of IoT sensor data, manufacturing process optimization, quality control analytics, and ML readiness is a pattern we have executed more than any other. We understand the organizational dynamics of scientific teams that are skeptical of data tools until they see them work. We start with a working prototype, not a slide deck.