Revvity has expanded the AI capabilities within its Signals software, embedding a new intelligence layer into the Signals One platform that lets scientists query and interpret R&D data using natural language. The move, announced June 23, 2026, targets a persistent bottleneck in scientific R&D: making sense of vast, disconnected datasets rather than simply collecting more information.
Moving beyond static dashboards
For decades, scientific software organized information into predefined applications, workflows, and dashboards. The upgraded Signals AI layer enables researchers to step beyond those static structures. They can now ask questions in plain language and receive context-aware answers drawn from connected R&D data, turning information into decisions without leaving their existing environments.
"The addition of the Signals AI capabilities within Revvity's Signals One platform reflects a fundamental shift in how scientists work with R&D knowledge," said Kevin Willoe, president of Revvity Signals Software. "For decades, scientific software has organized information into predefined applications, workflows and dashboards. The new features Signals AI introduces provide a new model where researchers can engage directly with organizational knowledge, ask questions in natural language and dynamically transform information. By combining the adaptive reasoning of modern AI with trusted scientific intelligence, Revvity's Signals AI helps organizations accelerate insight without compromising scientific rigor."
How Signals AI grounds responses in scientific rigor
The system is built on structured scientific data, domain ontologies, and validated scientific algorithms. This foundation ensures outputs are traceable and scientifically relevant. Researchers can generate interactive, context-aware views of molecules, sequences, experimental results, and connected datasets, which helps validate findings faster while maintaining the rigor required in scientific work.
This approach effectively redefines the role of scientific software, shifting it from a system of record into a system of understanding. The announcement is part of a broader trend in AI for Science & Research, where AI tools are moving from experimental to operational. As scientific software evolves, research scientists can deepen their skills with an AI Learning Path for Research Scientists to stay current with these changes.
Why this matters for science and research professionals
Select features of Signals AI are available now, with additional capabilities rolling out in the coming weeks. For researchers, the immediate impact is a reduction in the time spent searching for and cross-referencing data across siloed systems. By enabling real-time, natural language interaction with organizational knowledge, the tool helps teams move from insight to action more directly. The shift signals a move toward software that serves not just as a repository for past experiments, but as an active partner in the discovery process.
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