Deploying AI-Powered Analytics on Microsoft Azure

Customer Intro

A data-driven SaaS company specializing in analytics and customer intelligence sought a high-performance cloud infrastructure to handle growing datasets, deliver real-time insights, and support ML model deployment with minimal latency.

Problem/Goal

Their legacy infrastructure struggled to process large data streams efficiently and lacked native support for AI and machine learning workflows. Manual scaling and poor integration with modern analytics tools led to slow product performance, delayed reporting, and rising infrastructure costs.

Solution

We migrated their architecture to Google Cloud Platform (GCP), leveraging BigQuery for analytics, Cloud Functions for event-driven computing, and Vertex AI for managing ML models. Cloud Storage was used for data lake storage, and Pub/Sub enabled real-time data streaming. CI/CD pipelines were implemented via Cloud Build, and Stackdriver was used for centralized monitoring and alerting.

Results & Benefits

• 4x improvement in data processing speed
  
• 60% faster deployment of AI models
  
• 35% cost savings through auto-scaling and workload optimization
  
• Real-time analytics delivered with minimal delay to end-users
  

Insight

GCP’s native integration with data and AI tools makes it the cloud of choice for companies aiming to innovate faster and scale smarter in data-intensive environments.

Callout Quote

“With GCP, we’ve unlocked the ability to deliver real-time insights and scale AI features — without rebuilding our stack.”