Sustainability Product Market Share in 2022
Global Sustainability Market in 2023
Expected 5 Year CAGR for Sustainability Market
TensorIoT's deployment of a Generative AI solution has enabled the customer to automate key aspects of their operations, providing faster access to carbon emissions insights and improving their capacity to serve their clients' sustainability goals.
How can Generative AI be employed to automate key operations and enhance efficiency in sustainability reporting?
With our solution, the customer can now use a Large Language Model (LLM) to gain insights into carbon emissions, identify efficiencies, and uncover areas of potential improvement. The capability to generate expressions and analyze datasets using natural language brought significant value to their operations.
Our customer provides a platform that helps companies calculate their carbon emissions using data from various sources, such as utility bills, machine telemetry, and business units' input. Their goal is to aid their clients in meeting sustainability targets. They aimed to utilize Generative AI for automating significant parts of their operations, specifically extracting insights rapidly and asking data-driven questions using natural language.
The customer's platform was already an integral tool in assisting companies to achieve their sustainability objectives, but they faced the challenge of manually managing the vast quantities of data from various sources. This process was time-consuming and limited the speed and efficiency at which insights could be generated and decisions could be made. To address these issues, they identified a need to integrate Generative AI into their platform, with the specific goal of automating and accelerating the extraction of meaningful insights, and enabling natural language queries to efficiently interrogate data.
TensorIoT, in close collaboration with FlexZero, identified suitable Generative AI applications to augment their business processes. A targeted strategy was devised to create a tailored solution introducing a user-friendly interface capable of understanding natural language queries. This innovation, powered by AWS services like Amazon SageMaker, Amazon EC2, and Amazon Kendra, among others, facilitated quick extraction of carbon impact insights from data. The solution was built entirely on AWS, employing services like AWS Lambda for backend processing, Amazon API Gateway for secure and scalable API interactions, and Amazon RDS for reliable data management, ensuring a robust, scalable, and secure infrastructure.