Invert

What is Invert?

Invert is a specialized software platform designed to streamline the management and analysis of bioprocess data across the entire development lifecycle. It integrates seamlessly with lab hardware and software to automatically collect, clean, and standardize data from diverse sources, enabling biomanufacturing teams to analyze, model, and optimize their processes efficiently. Invert supports advanced statistical analysis, predictive modeling, and collaborative reporting, helping organizations reduce cycle times, improve data quality, and accelerate bioprocess development from R&D to production.

Key Features

• Automated Data Integration

  Connects with any bioreactor, lab equipment, ELNs, LIMS, and databases to ingest and standardize data automatically, eliminating manual data handling.

• Advanced Data Analysis

  Provides powerful statistical tools and visualization capabilities to explore and analyze bioprocess data without spreadsheets.

• Predictive Modeling

  Enables training of custom hybrid and meta-learning models on historical data to predict process outcomes and optimize experiments.

• Collaborative Reporting

  Generates audit-ready reports with interactive plots and calculations for easy sharing across teams and external collaborators.

• End-to-End Process Traceability

  Maintains full historical records with SOC2 compliance and ISO27001 certification, ensuring secure and reliable data management.

• Scalable Across Bioprocesses

  Supports diverse biological systems and product types, including cell therapy, synthetic biology, and contract manufacturing.

Use Cases

• Bioprocess Development : Accelerate design-build-test-learn cycles by unifying and analyzing experimental data to optimize bioprocess workflows.
• Cell Therapy Data Management : Standardize and analyze complex cell therapy data from multiple sources to improve process understanding and regulatory compliance.
• Contract Development and Manufacturing : Manage data across multiple projects and clients, leveraging meta-learning to reduce experimental data needs and speed up development.
• Scale-Up and Commercialization : Ensure data quality and traceability during scale-up to improve predictability and reduce risks in biomanufacturing.
• Regulatory Reporting : Organize and consolidate process data and metadata to facilitate IND filings and meet regulatory documentation requirements.

FAQs