At BigHat, we’re advancing an integrated approach to AI in biologics. Our platform unites state-of-the-art modeling with a high-speed automated wet lab, enabling rigorous benchmarking of AI models and rapid experimental validation across biologic modalities, targets, and therapeutic applications.
We’ve long recognized that no single model can address every challenge. A central pillar of our strategy is to match the right data and the right tools to each specific design problem.
And models are only as powerful as the data behind them. That’s why we’ve always prioritized generating comprehensive, high-quality antibody datasets, built with consistent protocols, automated pipelines, and rigorous QC to ensure reproducibility and scalability. These datasets fuel our growing “zoo” of models and provide the foundation for designing more innovative biologics.
Today, we’re excited to introduce Reccy Antibody Design Studio (RADS) - BigHat’s fully integrated AI platform that orchestrates the thousands of models and datasets in continual use across our programs. Fed by exceptional quality, high-throughput data from our Milliner™ platform and integrated with Reccy™, our custom LIMS++ data control plane, RADS continuously evolves our models by automatically updating and benchmarking model performance, demonstrating the impact of each AI-driven design loop.This integration provides our team and partners with unprecedented visibility - from project-level outcomes down to individual sequences and their predicted properties. With RADS, we directly link modeling and experimentation, creating a robust and scalable foundation for biologics, from discovery through clinical candidate.
