Case Study
Driving Growth with iSERV for a Leading AI & Machine Learning Company
0
%
reduction in operational costs
About the Client
A leading AI and machine learning company that offers predictive analytics solutions to help businesses optimize operations, improve decision-making, and drive growth. Faced with challenges like managing large volumes of machine-generated data, creating intuitive dashboards, and implementing advanced predictive models, the company partnered with ITHENA to deploy the iSERV Smart Service Solution. By leveraging AI-powered insights and persona-centric dashboards, they transformed data into actionable intelligence, improving operational efficiency, increasing revenue, and enhancing decision-making capabilities.
0
%
reduction in operational costs
ITHENA’s iSERV solution has been instrumental in transforming our data management and predictive analytics capabilities. The persona-based dashboards and AI-powered models have empowered us to deliver actionable insights to our clients, improve asset efficiency, and significantly reduce operational costs. This partnership has accelerated our mission to help businesses optimize and grow.
– Director, Technology
– Director, Technology
Opportunity for Impact
Reduction in operational costs
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%
Rise in asset efficiency
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%
Boost in revenue growth
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%
Key Challenges Faced
- Difficulty collecting large volumes of data from large vehicular machines
- Inability to provide asset data to end users in the form of dashboards
- Lack of AI/ML models for persona-centric dashboards
Our Solution
ITHENA addressed the client’s challenges with iSERV solution:
Persona-based Dashboard
Enabled the integration of predictive models to create user-friendly, persona-based dashboards
Performance Monitoring
Empowered accurate data collection, analytical model execution, and customized visualization of asset performance
Predictive Maintenance
Optimized inventory with digital logbooks, streamlined checklists, and predictive analytics
AI-powered Solution:
Implemented AI/ML models with a black-boxing feature to predict total accumulated wear and fuel consumption
