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Case Study

Driving Growth with iSERV for a Leading AI & Machine Learning Company

An AI and machine learning company struggled to collect data from large vehicular machines and turn it into useful dashboards. iSERV added data collection, persona dashboards, predictive maintenance and AI/ML models.

Engagement at a glance
Client
AI and machine learning analytics company
Industry
Technology
Solution
ITHENA Smart Service (iSERV)
Scope
Machine data collection, persona dashboards, predictive maintenance, AI/ML models
Headline result
35% lower operational costs
Opportunity for impact

Three numbers the platform moved.

Results after iSERV connected the client's machine data.

35%

Reduction in operational costs

Automated data collection and dashboards cut manual effort.

27%

Rise in asset efficiency

Predictive models helped get more out of each machine.

32%

Boost in revenue growth

New data products for end users supported revenue growth.

About the client

Meet the organization and understand the context behind the transformation.

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.

In their words
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
Key challenges faced

Machine data that never reached users.

Large machines produced huge data volumes, with no dashboards or models to make them useful.

  • 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

How ITHENA closed the gaps.

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

Inside the console

Machine data, turned into products.

An illustrative iSERV view: data from large vehicular machines feeds persona dashboards and predictive models for end users.

iserv.ithena.app/fleet/vehicle-machines
siSERV FleetDashboardsModelsReports 3 OP
Models/Heavy vehicle fleet/Last 12 weeks
Machine data processed and predictions served, last 12 weeksPrediction accuracy climbing
Wk 1Wk 6Wk 12
Data points processedPrediction accuracyDashboard users
Collecting data from large vehicular machines in the field · 09:41:07
Since iSERV went liveLive
35%Operational costs, down
27%Asset efficiency, up
32%Revenue growth
240Machines streaming data
Model usage by persona, share
Fleet managers34%
Service engineers27%
Operators19%
Executives12%
Partners8%
Model activity3 new
Wear model retrained on last month’s data12m
Hydraulic pressure anomaly predicted on machine 11830m
Persona dashboard published to fleet managers1h
Fuel efficiency report shared with end customers2h
Data gap detected on 2 machines, sync retried3h

Illustrative view. Unit names, counts, shares and activity are examples; the headline results are the client's.

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