What changed after release for BluePi customers.

BluePi built a Google Cloud MVP that captured changes from MySQL, PostgreSQL, and MongoDB with Debezium Server. Optimized BigQuery procedures made current operational data available for reporting at a lower cost and shorter runtime.

Housing’s data and reports were distributed across departmental systems, leaving teams with conflicting KPI definitions and limited access. BluePi consolidated the data in Snowflake, standardized KPIs, and introduced role-based access and cataloguing.

DTDC relied on manually assembled reports across sales, marketing, and operations systems. BluePi built a central Google Cloud reporting path with Dataflow pipelines, BigQuery, and Google Data Studio dashboards for MIS, bottleneck analysis, and senior-management KPIs.

A beverage bottler lacked a consistent view of primary sales, secondary sales, inventory, finance, assets, and distribution. BluePi integrated SAP and commercial data on a governed cloud platform and automated reporting.

Compass Group worked with inconsistent records and conflicting reports across SAP and related systems. BluePi integrated inventory, sales, and operational data on AWS Redshift, established master-data workflows and governance, and applied field-level quality controls.

Lenskart partnered with BluePi to design a cloud-native enterprise analytics platform on Google Cloud, supporting more than 2 TB of daily ingestion, 6000+ MySQL tables, and near real-time CDC latency for retail operations.

BluePi built store-level order and ingredient forecasts for every hour of the operating day. Restaurant teams used the forecast to prepare earlier, adjust for local conditions, and reduce food waste.

Yum Brands needed to understand changing sales drivers and test response scenarios during a period of rapid demand disruption. BluePi built a cloud analytical path with SQL Server, Amazon EC2, Amazon EMR, and Power BI and released it in a matter of weeks.

A QSR franchisee worked across disconnected point-of-sale, SAP sales and inventory, HR, and related systems. Conflicting measures and manual reconciliation delayed financial and operating reports. BluePi integrated the sources in Snowflake, added continuous data-quality checks, and built transaction-matching workflows for investigation and correction.

A pharmacy benefits provider depended on a decades-old application whose interdependent code had become difficult to maintain and scale. BluePi used a strangler-pattern transition and refactored selected application areas into cloud-native service boundaries.

Financial-market participants needed a controlled way to create, negotiate, approve, update, and report on pre-trade FX agreements. BluePi designed a platform supporting multiple agreement types, configurable workflows, offline participants, bulk changes, risk-system integration, and GraphQL reporting.

Post-trade users relied on connections spread across applications built by different teams and inherited through two decades of organizational change. BluePi designed one platform for onboarding, connection status, service monitoring, trading-partner visibility, and alerts.

A multi-brand QSR operator relied on manual spreadsheet pivots and distributed reports for daily measures. BluePi built streaming and batch data paths, Snowflake reconciliation, Java APIs, and a custom React cockpit that brought KPI visibility to approximately one-minute latency.

Dainik Bhaskar needed a secure on-demand video platform that could support a large audience and potential distribution on future devices. BluePi connected ingestion, metadata, real-time transcoding, adaptive streaming, protected delivery, playback, advertising, and subscriptions.

Pizza Hut’s online ordering application had suffered slow page loads for four months. BluePi monitored the running system for two days, isolated session management in the web tier as the focused intervention, and validated the change under concurrent load.

InfoDrive India’s private-cloud application had manual deployments, limited scaling, and weak operating visibility. BluePi moved the application to AWS, automated delivery, used load testing to right-size capacity, introduced autoscaling, and added application and infrastructure monitoring.

Nearbuy wanted to move from reactive incident handling to a preventive reliability and cost program. BluePi established service levels, monitored critical AWS resources, optimized services and Spot capacity, and automated recurring defect detection and resolution.

ChannelPlay needed a tested business-continuity path for applications exposed to single points of failure. BluePi migrated the core estate to a multi-tier, Multi-AZ AWS architecture and implemented disaster recovery with RPO and RTO targets under one hour.

qMetrix needed a repeatable release path for an application changed by several developers. BluePi connected AWS CodeCommit, CodeBuild, CodeDeploy, and CodePipeline so code and configuration could move through test and production without manual deployment steps.

The manufacturer’s Oracle warehouse and OBIEE reporting path suffered downtime, delayed data, peak-period failures, and weak integration. BluePi migrated the warehouse to Snowflake, replaced OBIEE with Power BI, integrated SAP finance, and added continuous Fivetran replication.

BluePi connected toll transactions, NPCI files, vehicle images, automated processing, reporting, and violation workflows in one AWS-based platform. The system reduced manual reporting and supported vehicle-class validation and reconciliation.

Seventy percent of the organization’s customer queries repeated questions support executives had already answered. BluePi designed an AI-enabled chatbot using structured question-and-answer content and natural-language understanding across digital and voice channels.

A distributor wanted to improve product discovery across a 16,000-dealer network. BluePi prepared catalogue, dealer, purchase, and SKU data on AWS and built recommendation paths for related products, similar-dealer behavior, and seasonal stocking.

BluePi unified CaratLane’s customer signals in an AWS data platform and delivered personalized recommendations while shoppers were still active. The add-to-cart conversion rate on personalized interventions doubled.

DHFL Pramerica needed a consolidated customer view and a repeatable way to assess risk before recommending insurance products. BluePi built a machine-learning path from life-policy data through AWS Glue, Amazon S3, Amazon EMR, and Amazon Redshift. The system produced high-, medium-, and low-risk customer segments from demographic, education, health, and policy attributes.