Biologics Characterization

Accelerate and improve biologics characterization

Discover how to identify and monitor CQAs faster, better


process development

More accurately

defined QCAs and CPPs

AI-native data

for predictive modeling

Automated processes

Connect all data sources and targets seamlessly, boosting speed and data quality

Centralized, enriched data

Find previous results fast with searchable data in the cloud and avoid repeating experiments

Ready for AI

Model the CQA-CPP relationship to accurately assess risk and identify deviations earlier

Labor-intensive processes

Manual data handling and transcription in characterization studies is slow and error prone

Inaccessible historical data

Time is wasted trying to retrieve past data, or repeating tests when unavailable

Dead-end datasets

AI can’t analyze raw data to identify CPPs faster and monitor CQAs more effectively

Unlock the full value of your scientific data


Collect and centralize data from all instruments and software


Monitor and trend CQAs with visualization and analytics tools


Contextualize and harmonize the data for search and analytics/AI


Use AI/ML to understand how CPPs impact CQAs, leading to better QC

Get a demo

How to free your data from isolation

Explore how dispersed scientific data can easily be accessed, enriched, and harmonized for analytics and AI/ML with the Tetra Scientific Data and AI Cloud. This on-demand webinar features multiple case studies.