Revvity joins R&D’s move toward MaaS, touting intelligence as a service

Revvity, a science technology company, announced the introduction of a new Model-as-a-Service (MaaS) called Signals Xynthetica on Tuesday, adding to a growing trend that is democratizing predictive modeling.

Instead of building models, which is expensive and requires specialized skills, companies are now subscribing to pre-trained, high-fidelity models. This allows labs that couldn’t afford to build their own model to access predictive models.

In addition, MaaS integrates AI tools directly into the experimental workflow, eliminating the need to transfer data from an ELN, which reduces human error. MaaS eliminates the need for large teams of data scientists and IT infrastructure. The models also remove the necessity of sending sensitive data to a public AI firm, keeping R&D data secure.

The newest MaaS: Revvity’s Signals Xynthetica

Revvity’s Signals Xynthetica will enable AI-augmented molecular and material design, bringing in-silico generation, predictive modeling and experimental validation into a single environment. The software is intended to address the gap between powerful models and high-quality experimental data by embedding models directly into the scientific context, connecting AI predictions with wet-lab outcomes.

The model also enables a “loop” effect as it applies insights from results to improve experimental design. Models like this allow scientists to test molecules for target effects virtually before they conduct expensive wet-lab experiments, closing the DMTA cycle. Researchers can now accomplish all of this without having to write a single line of code with technologies like Signals Xynthetica.

The growing MaaS market

Industry giants are jumping on the trend, too. AWS’ SageMaker provides an integrated platform for analytics and AI. It also allows for collaboration with a unified studio for model development in SageMaker AI. The platform hosts an AI assistant called Amazon Q, which AWS calls “the most capable generative AI assistant for software development.”

Google’s MaaS, Vertex AI, utilizes Gemini model series to build generative AI apps in Vertex AI Studio, a Google Cloud–based platform. Vertex AI notebooks are integrated with Google Cloud’s data warehouse platoform BigQuery to provide a single system across all data and workflows. BigQuery is an autonomous data-to-AI platform that automates the lifecycle from ingestion to insights.

According to a report from Prophecy Market Insights, the MaaS market reached $17 billion in 2024. The report predicts the market will grow to $491.5 billion by 2035, with a CAGR of 36.2%.