VuNet Systems leverages Azure to provide observability and AI-driven insights to customers

Need to manage large volumes of data in the new normal

When the pandemic struck, the number of digital transactions increased manifold, thereby multiplying the volume of online payments. All enterprises now needed the ability to handle increased workloads to create a superior user experience for every single transaction, indirectly increasing the need for better end-to-end visibility systems.

This sudden surge of transactions, increased complexities of the existing infrastructure and was impacting VuNet’s customers in terms of increased downtime due to lack of visibility and affected the customer experience for their end-users. For instance, problems such as 3dSecure / 2FA / OTP taking too long to be received or shipment prompts not showing up after payment became frequent.

Leveraging Azure for seamless data handling and analytics 

VuNet leveraged Microsoft Azure to provide holistic visibility into end-to-end transaction performance cutting across various data silos, simplifying the operational complexity of managing a distributed application and infrastructure layer.  Azure’s flexible and scalable infrastructure also allows VuNet's integrated platform to deploy domain specific ML models at scale to provide the proactive and intelligent insights across business journeys in real time. 

VuNet’s AIOPs platform allows seamless creation of data pipelines and integrates ML Ops functionality for testing and deploying models. PaaS components like Azure Data Factory and Azure Blobs were deployed to accelerate the platform’s speed and efficiency. 

Benefits

With Azure, VuNet is now monitoring and providing analytics for Terabytes of logs and metrics per day.  This has saved 1000s of hours of time and helped the operations team to get more proactive, accelerate their troubleshooting, improve the system uptime, and optimize vastly their transaction performances by more than 60%. 

Factors enhancing employee efficiency:

• Rolling out beta releases and demos quickly
• Enabling teams to adapt to sudden spike and dips in data 
• Re-shifting focus on the core functionality of the product
• Facilitating global deployment 
• Hyperscale infrastructure options to handle workloads at scale
• Faster provisioning and time to value for end customers

Factors enhancing the customer experience:

• Superior end-user experience with higher system uptime
• Reduced technical declines through ML-led proactive and predictive insights
• Faster remediations with real-time root cause analysis and faster error reversals
• Enhanced revenue potential through business- and domain-specific data enrichments
• Increased cross-team collaboration