Revr

Revr provides a platform for functional protein design streamlined by combining advanced AI systems with real-world, large-scale biophysical data collected from its automated wet lab. Unlike traditional AI models that rely mainly on public datasets with limited experimental evidence, Revr integrates direct laboratory data into its design and optimization process, ensuring that each protein is functionally validated and iteration cycles are significantly accelerated.

The system offers a closed-loop process, where high-throughput experimental testing generates granular activity data used to train and improve their AI models continually. As the platform produces and validates nearly a thousand proteins per week with extensive functional testing, every new data point obtained directly enhances their AI’s ability to make accurate, meaningful design choices for novel proteins.

Revr's API-based, cloud-enabled infrastructure allows for efficient data sharing and integration while building a unique dataset unattainable to others in the field. The solution is suited to organizations seeking to speed up protein engineering programs—from initial design to laboratory-confirmed function—all within much shorter development timelines than competing approaches. Designed for scalable, robust protein design, Revr aims to make experimental validation an integral part of AI-driven biotechnology workflows.

This platform is especially relevant for biotechnology and pharmaceutical companies engaged in protein engineering and synthetic biology. Heads of R&D, Chief Technology Officers, and product managers responsible for developing novel proteins for therapeutic, industrial, or diagnostic applications will find value in the accelerated iteration cycles and laboratory-validated outcomes.

It’s designed to meet the needs of teams who require accurate prediction and rapid refinement of protein function, whether for drug discovery, enzyme engineering, or custom bioproduct development. Operations and laboratory managers interested in integrating automated data capture with AI-driven design will benefit from the closed-loop workflow and high experimental throughput.