Tredence’s New Energy.AI Solution to Help Reduce Emissions

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Pure-play AI and analytics firm, Tredence, today unveiled Energy.AI, a solution that aids telecom, technology, manufacturing, and CPG companies in achieving their energy efficiency goals and meeting ESG and UN Sustainable Development targets. 

Energy.AI enables enterprises to monitor, capture, and optimize their energy consumption, leading to better operational efficiency and faster response times, with a guaranteed minimum increase of 30%, Tredence claims. 

It harnesses the capabilities of AI and provides businesses with up-to-the-minute insights to simplify procedures, reduce emissions, optimize energy usage, and achieve sustainability targets with ease.

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The company claims to have helped a significant telecom enterprise enhance asset performance by 17%, decrease energy consumption by 10%, and save approximately $100 million in energy costs.

Currently, the telecommunications industry contributes 1.6% of the world’s carbon dioxide emissions, and a majority of these emissions (60%) stem from their energy consumption.

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Lakshmi Ramamurthy, Head of Telecom, Media, and Tech at Tredence, said, “Energy.AI delivers maximum value for telecoms striving to achieve sustainability, while optimizing energy emissions and costs.”

“In as little as six weeks, you can use a digital-twin powered energy efficiency solution that leverages prescriptive and predictive methods to reduce the carbon footprint of your value chain,” she added.

Energy.AI offers a complete perspective of energy usage and anticipatory insights, offering organisations a comprehensive outlook of their energy consumption. This allows it to identify high-emission sites, evaluate risks, and rapidly take action to decrease energy consumption. Energy.AI’s root-cause analysis and intelligent recommendations aid in efficient and effective decision-making, propelling sustainability objectives forward.

The telecom industry accounts for 1.6% of global carbon emissions, while the tech sector contributes 2 to 3%, and manufacturing is responsible for almost 20%. In light of this, Energy AI is a much-needed and reliable initiative to lower emissions and optimise energy efficiency.

Recently, Tredence had also announced the release of their AI-Driven platform, Atom.AI. Atom.AI uses Databricks Lakehouse to accelerate AI applications for retail and consumer packaged goods.

Atom.AI asserted that it can cut down the time it takes to generate value from data by 50%. This is accomplished by utilising its AI and machine learning capabilities, pre-designed feature store, technical notebooks, extensive data sets, and third-party connections.

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Shyam Nandan Upadhyay
Shyam is a tech journalist with expertise in policy and politics, and exhibits a fervent interest in scrutinising the convergence of AI and analytics in society. In his leisure time, he indulges in anime binges and mountain hikes.

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