The Internet of Things (IoT), Machine Learning and Artificial Intelligence (AI) revolution is already transforming many business processes and will transform many more in the near future. Our AI and Machine learning based platform helps to accurately forecast demand for service and create a service ecosystem that is efficient, nimble and prescriptive. By using the internal enterprise data, asset details, customer attributes, service Bill of Material, and device logs, our advanced machine learning models and big data analytics turn the asset management operations from reactive to proactive.

How we do it

Accrete has a deep domain expertise and experience to deploy IoT/AI solutions with speed and scale. Our partner ecosystem and talent pool can customize solution specific to each client needs. Our services include all steps required to transform service operations using IoT/AI technology.

Digital Service Transformation

The digital disruption signals like IOT, AI, Blockchain and customers’ ever-increasing expectations and their influencing power, requires a fresh look at service strategy. We have extensive experience of providing services for such a service digital transformation, leveraging our multi-year experience in service processes & customer experiences, best practices and our technology expertise.

Proof of Value (POV)

We perform a compelling POV to enable you to get comfortable with our products and service offerings. The three-step process that we perform as a part of the POV are:

  • Data provision & ingestion
  • Machine learning activation and modeling
  • Failure prediction report generation

Problem Identification and Notification

Connected devices provide the ability to monitor asset conditions such as usage, temperature, pressure and other parameters in real- time. Any deviations beyond the specified range of parameters can trigger alerts or notifications for proactive action. Alerts and notifications can prevent disasters and help in cost reduction. Notifications can also be automatically categorized based on device fault codes. Furthermore, we can apply machine learning models to provide proactive alerts before the deviation becomes significant enough to cause unscheduled downtime.

Success Stories