Digitisation

Revvity launches Signals LabGistics to automate scientific workflows

A unified lab ecosystem drives innovation across life and materials sciences through coordinated scientific workflows

  • By IPP Bureau | October 20, 2026

Revvity has launched the Signals LabGistics solution through its Revvity Signals Software business. This AI-first, scientific workflow orchestration platform is designed to connect and coordinate processes, workflows, and tasks all the way from discovery to manufacturing.

Signals LabGistics uses AI-powered workflow design to translate standard operating procedures (SOPs) and natural-language descriptions into coordinated scientific workflows. This solution solves a major bottleneck for pharmaceutical, biotechnology, and specialty chemicals organizations. While these industries pursue complex innovations like novel therapeutics and advanced materials, they are often slowed down by fragmented systems and manual handoffs, particularly during the critical technology transfer from R&D to manufacturing.

Signals LabGistics streamlines scientific workflows by connecting your teams, systems, and data in one intelligent platform. By providing real-time insights in a single digital environment, it contextualizes decision-making and drives continuous process improvement.

“Scientific innovation is increasingly dependent on the ability to coordinate work across people, systems and stages of development,” said Kevin Willoe, president of Revvity Signals Software. “Our Signals LabGistics offering brings those moving parts together and helps organizations move promising therapies and new products forward with greater speed, consistency and confidence.”

Beyond operating within the broader Revvity Signals Software ecosystem, Signals LabGistics integrates seamlessly with your current laboratory and manufacturing platforms, while easily scaling to adjacent systems in the future.

Combined with multi-tier execution and real-time monitoring, these capabilities optimize the flow of information, samples, and activities across independent teams. As a result, scientists and managers gain clear visibility into workflow status, allowing them to spot emerging bottlenecks early and act before delays disrupt broader development timelines.

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