Developments in automation, and AI and machine learning has been a significant contributor to the emergence and rapid adoption of IoT across enterprises. The technology undoubtedly makes industries smarter and lives easier, but also has its drawbacks. Most significant of the concerns is the enormous amount of acquired and generated data pertaining to these devices and the need to find appropriate ways to secure it. Another challenge that most enterprises face is their habitual manual practices that makes data analysis and operational procedures slow and less productive. In light of this, it becomes vital for companies to adopt strategies to ensure that operational data is analyzed accurately and well curated in accordance with their needs. Addressing these requirements, GreenTropism leverages cutting edge spectroscopy in its embedded solutions. The innovations and application of Spectroscopy help provide convenient, predictive and precise spectral data analysis robustly and cost-effectively. “We aim to provide smarter and more efficient work processes, with intelligent IoT devices connecting their entire operational ecosystem,” states Anthony Boulanger, the CEO of GreenTropism.
Spectral data analysis and processing form the core of GreenTropism’s offering, which propels the company to employ spectroscopy into its solutions which provides unmatched accuracy when it comes to predictive analysis of the collected data. The technology seemed very far-fetched initially, due to its excessive cost and increased storage space.
We are not just managing and processing but proposing a full solution to go from the database to the selection of sensors and to the way we communicate with the sensors and deliver the final analysis
Eventual miniaturization has not only reduced the production cost but has helped spectroscopic sensors and their outputs to be embedded with ease into the intranet, company networks, and factory floors. The driving force behind the software solutions of GreenTropism is its algorithms. These are created by the company to be used as inputs to interpret spectroscopic data or the spectra. The varied groups of algorithms process and translates the spectral databases, that can be readily employed to enable industrial components to take decisive actions, independently and automatically.
A major aspect of GreenTropism’s offerings is that it enables additional process metering that provides industries with plug and play devices. The GT-Controller is an essential offering of the company as it successfully negates the long-drawn processes of streamlining the algorithms to align with the clients’ needs. It facilitates simpler and more accurate data integration with real-time projections, productivity analysis, to ensure a consistent workflow. Each GT-controller is equipped with routers and can easily connect to the GT-Cloud for storing the curated data. Another important offering of GreenTropism is the GT-DataManager, a collaborative software to create and architecture spectral databases which help to optimize R&D projects by enabling them to create, store and manage their own database and library of algorithms. The offering provides management and control of spectral data all the way from the spectrometer’s output to its use, through a spectral viewing system and a query portal. This system can be integrated with the existing infrastructure of companies or deployed in the cloud via secure access.
GreenTropism is moving toward enriching database creation and improving instantaneous analysis capabilities by focusing on commercial processes, with the best interests of its clients in mind. The launch of their new software GT-DataManager and innovations in upgrading GT-Controller is another leap for the company in its holistic approach to revolutionize IoT implementations with spectrometry across various industries including agri-food, chemical, petrochemical, cosmetics, environment, textile, automotive and many more. “We are not just managing and processing but proposing a full solution to go from the database to the selection of sensors and to the way we communicate with the sensors as a network and deliver the final analysis,” concludes Boulanger.