Data Replatforming

TetraScience's Data Replatforming solution, part of the Tetra Scientific Data and AI Platform, enables life sciences organizations to collect, centralize, and contextualize all of their scientific data in a purpose-built cloud environment. Designed as the essential first step in a scientific AI journey, it automates the assembly and movement of scientific data from instruments, informatics applications, analysis software, and partners into a single, unified source of truth — while automating data flow to any downstream applications of choice.

Unlike traditional Scientific Data Management Systems (SDMS), the Tetra Scientific Data and AI Platform is cloud-native, data-centric, and vendor-agnostic. It transforms raw scientific data into AI-native Tetra Data — the atomic building blocks for Scientific AI — enabling organizations to address immediate lab connectivity and data management needs while simultaneously building a foundation for advanced analytics, machine learning, and AI.

Key Advantages Over Traditional SDMS

• Cloud-native architecture reduces total cost of ownership, provides scalability, and supports compute-intensive workloads for analytics, AI, and ML — replacing on-premises deployments.
• Engineered data and lakehouse approach enables scientific insights through large-scale, multi-modal analysis, moving beyond simple file-based storage.
• Data-centric and vendor-agnostic design avoids data silos and vendor lock-in, building a foundation of data liquidity for AI and analytics.
• Event-driven data processing and engineering delivers flexible, customizable, on-demand data processing and information extraction, replacing limited inline processing.
• Colocated data and applications in a data workspace improve scientific productivity with seamless access to data and a growing ecosystem of data applications.
• Collaborative and multi-company capabilities enable secure data sharing across an ecosystem of partners with liquid data.
• Foundation and onramp for analytics and AI fulfills immediate connectivity and compliance needs while enabling advanced analytics and AI — rather than treating data management as an end state.

Core Data Replatforming Capabilities

• Collect: Automate the collection of data from scientific instruments, informatics applications, analysis software, and partners. Leverage industrialized integrations for a growing set of data sources and targets, avoiding time- and resource-consuming do-it-yourself approaches.
• Contextualize: Automatically extract essential metadata and enrich scientific data. Use scientifically relevant metadata to enable easy search and retrieval, and to power meaningful analytics and AI.
• Centralize: Eliminate inaccessible data silos and assemble large-scale datasets. Replatform scientific data into a centralized, single source of truth, and publish data directly to consumers for easy reuse and insight generation.
• Data Analytics and Quality: Ensure high data quality and maintain data integrity and traceability. Streamline compliance with regulations such as 21 CFR Part 11 and GxP guidelines using automation capabilities, validated integrations, and extensive documentation.

Scientific Workflow Automation

• Remove erroneous manual transcription, tedious data manipulation, and laborious data verification and review processes.
• Automate common scientific workflows while improving compliance and data integrity.
• Support the full design-make-test-analyze (DMTA) cycle by collecting data produced at every step of the scientific research process.
• Link data related to measurements, context, methods, and analysis, and prepare it for analytics and AI applications.

Integrations Library

• Access one of the most sophisticated and fastest-growing libraries of scientific integrations, covering both instruments and informatics applications.
• Instrument integrations include: AGU Sm@rtLine Data Cockpit (SDC), Beckman Coulter Vi-CELL Cell Counter, Beckman Coulter CytoFlex, BMG LABTECH microplate readers, Cytiva UNICORN, HighRes Biosolutions Cellario, Mettler Toledo LabX, PTC KEPServerEX, ThermoFisher Scientific Chromeleon, ThermoFisher Scientific Xcalibur, Waters MassLynx MS software, and Waters Empower CDS.
• New integrations and updates are continuously added, with a full library and upcoming releases available for review.

The Tetra Scientific Data and AI Platform is deployed as a cloud-native solution and is designed to support leading biopharma and life sciences organizations in streamlining workflows, improving scientific outcomes, and driving innovation. It is built to meet regulatory compliance requirements including 21 CFR Part 11 and GxP guidelines, and supports secure multi-company collaboration across the R&D ecosystem.