Revvity, Inc. has officially announced the upcoming launch of Signals Xynthetica Models-as-a-Service (MaaS), a new artificial intelligence offering that will be delivered through the Revvity Signals platform. With this move, the company aims to significantly enhance AI-augmented molecular and materials design by bringing together advanced computational modeling and real-world experimental validation within a single, governed environment.
As artificial intelligence and machine learning continue to reshape life sciences and materials research, discovery efforts are rapidly transitioning away from traditional trial-and-error approaches. Instead, organizations increasingly rely on predictive, data-driven design methods to improve accuracy and efficiency. However, despite the availability of sophisticated algorithms, many AI solutions struggle to deliver real-world value because they lack deep integration with high-quality experimental data and established scientific workflows. Signals Xynthetica is designed to close this gap by embedding predictive models directly into the everyday environments scientists already use.
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Through the new Signals Xynthetica offering, scientific teams can iteratively design, test, and refine candidate molecules and materials with growing confidence. The platform integrates in-silico generation, predictive modeling, and experimental validation into a continuous feedback loop. As a result, AI predictions evolve alongside wet-lab outcomes, allowing models to learn and improve as new data becomes available.
Moreover, the platform will support a broad range of in-silico design strategies, including de novo molecular generation, property prediction, and multi-objective optimization. Delivered as a Models-as-a-Service solution, Signals Xynthetica removes the need for organizations to build or maintain complex AI infrastructures. Instead, users can easily access, govern, and apply predictive models across multiple projects, ensuring consistency and transparency throughout the discovery process.
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Another key advantage of the MaaS approach lies in governance and scalability. Models can be refreshed as new experimental data emerges and evaluated side by side with lab results. This structure allows teams to maintain scientific rigor while scaling AI-driven discovery across departments and research programs.
“AI has enormous potential to transform how molecules and materials are designed, but that potential is only realized when models are connected to real scientific workflows and data,” said Kevin Willoe, president of Revvity Signals Software. “The Signals Xynthetica platform is about operationalizing predictive science that brings in-silico design and experimental validation together in a continuous loop, accelerating discovery across industries.”
Looking ahead, Revvity expects Signals Xynthetica to become a foundational element of its broader strategy for AI-augmented discovery. The offering is designed to help customers harness predictive capabilities at scale while preserving strong governance, responsible data stewardship, and scientific integrity.
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