Technology company with social impact and innovation
Technology Brilliance

Capability: Data & AI

Introduction

Travel Data Warehouse Modernization enables travel technology companies to move beyond legacy reporting systems and unlock scalable analytics capabilities. Many travel platforms rely on traditional data warehouse architectures that struggle to process large volumes of commercial and operational data generated across booking systems, sales channels, and partner networks. As a result, analytics initiatives slow down and organizations lack visibility into real-time sales performance.

This case study highlights how a travel technology firm modernized its existing data warehouse by implementing a cloud-based data lake on AWS. By redesigning its data architecture and integrating key sales data sources, the organization improved visibility into commercial performance, accelerated analytics adoption, and established a scalable data platform capable of supporting future advanced analytics and AI initiatives.

Customer

The customer is a travel technology firm providing digital solutions and platforms that support travel commerce and booking ecosystems. The organization manages large volumes of sales and business data generated across multiple channels and services.

Over time, the existing data warehouse environment became difficult to scale and limited the organization’s ability to analyze sales performance efficiently. As the company expanded its analytics ambitions, it required a modern data platform that could support flexible data integration and advanced analytics capabilities.

Business Objective

The primary objective was to modernize the existing data warehouse architecture and transition toward a scalable cloud-based analytics platform.

The organization aimed to implement a cloud data lake that could support growing data volumes and enable new analytics use cases around sales performance. In addition, leadership sought to improve visibility into sales trends and overall business performance across the organization.

Another key goal was to establish a flexible data foundation that could support future analytics initiatives and evolving business requirements.

Scope of Services

The engagement focused on implementing a modern cloud data platform, including:

  • Design and implementation of a cloud-based data lake on AWS

  • Modernization of the existing data warehouse into the new data lake architecture

  • Integration of sales and related business data sources

  • Enablement of analytics capabilities to support sales performance insights

  • Optimization of data pipelines for scalability and improved performance

Benefits

  • Modern and scalable cloud data platform supporting evolving analytics needs

  • Improved visibility into sales performance and business trends

  • Faster access to analytics and reporting capabilities

  • Reduced limitations associated with legacy data warehouse systems

  • Strong data foundation supporting advanced analytics and future AI use cases

Impact

  • Enhanced analysis of sales performance across the organization

  • Improved data-driven decision-making

  • Increased agility in responding to market and business trends

Introduction

Healthcare Data Warehouse Modernization to AWS enables healthcare providers to move beyond legacy data infrastructure and unlock scalable analytics capabilities. Many healthcare organizations rely on on-premise data warehouses that limit flexibility, increase operational costs, and slow down analytics initiatives. As healthcare systems generate increasing volumes of operational and clinical data, modern data platforms become essential for enabling faster insights and supporting data-driven decision-making.

This case study highlights how an Australian low-cost healthcare provider modernized its data warehouse environment by migrating to a scalable cloud platform on AWS. By transforming its legacy data infrastructure into a modern cloud-based analytics platform, the organization improved accessibility to enterprise data, accelerated analytics adoption, and strengthened its readiness for advanced analytics and AI initiatives.

Customer

The customer is an Australian healthcare provider focused on delivering cost-effective healthcare services while maintaining operational efficiency across its organization.

As the healthcare group expanded its services and operations, its legacy on-premise data warehouse environment began limiting scalability and analytics capabilities. Fragmented systems and infrastructure complexity slowed reporting and reduced visibility into operational performance. Therefore, the organization required a modern data platform capable of supporting analytics growth and future innovation.

Business Objective

The primary objective was to evaluate and implement a cloud-based modernization strategy for the organization’s existing data warehouse.

The healthcare provider aimed to migrate its legacy on-premise data warehouse to a scalable cloud platform that could support growing data volumes and advanced analytics initiatives. In addition, leadership wanted to improve operational insights and enable faster data-driven decision-making across the organization.

Another key goal was to establish a stable, secure data platform that could support long-term analytics needs while ensuring ongoing operational reliability.

Scope of Services

The engagement focused on end-to-end data warehouse and analytics transformation, including:

  • Feasibility analysis for cloud-based data warehouse modernization

  • Migration and modernization of the legacy DWH to AWS

  • Design and development of analytics capabilities on the cloud platform

  • Establishment of scalable and secure cloud data architecture

  • Ongoing platform support and operational maintenance

Benefits

  • Scalable and cost-efficient cloud data warehouse platform

  • Improved accessibility to enterprise data across the organization

  • Faster generation of insights for operational and clinical decisions

  • Reduced complexity associated with legacy data systems

  • Reliable platform operations supported by continuous maintenance

Impact

  • Accelerated adoption of analytics capabilities

  • Improved data-driven decision-making across the organization

  • Enhanced operational efficiency

  • Greater readiness for AI and advanced analytics initiatives

Introduction

Commercial Data Warehouse Migration to AWS enables airlines to modernize legacy data environments and support scalable, data-driven decision-making. Many aviation organizations operate complex data warehouse ecosystems built on legacy BI stacks and tightly coupled systems. Over time, these environments become difficult to maintain, expensive to operate, and slow to support new analytics initiatives.

This case study highlights how a large European airline modernized its commercial data ecosystem by migrating its legacy Data Warehouse (DWH) to a cloud-native architecture on AWS. By transforming the legacy platform into a scalable Data Lake and simplifying commercial data workflows, the airline improved data quality, enhanced governance, and enabled faster decision-making across pricing, revenue, marketing, and sales domains. As a result, the organization established a future-ready data platform capable of supporting advanced analytics and AI-driven innovation.

Customer

The customer is a major European airline managing large volumes of commercial and operational data across multiple business units. The airline’s legacy commercial Data Warehouse had grown overly complex due to years of system dependencies, custom pipelines, and fragmented reporting environments.

These challenges made it difficult to maintain data quality, slowed analytics initiatives, and increased operational costs. Therefore, the airline required a modern, cloud-based data architecture that could simplify the commercial data landscape while enabling scalable analytics capabilities.

Business Objective

The primary objective was to modernize the airline’s commercial data ecosystem by migrating its legacy Data Warehouse to a scalable cloud-native Data Lake on AWS.

Key objectives included reducing dependency on legacy BI tools and high-cost infrastructure, simplifying the commercial data landscape, and eliminating interdependency-driven bottlenecks. In addition, the airline aimed to improve data quality and governance while enabling cross-domain visibility across commercial functions.

Another important goal was to support faster decision-making through self-service analytics and unified reporting. Ultimately, the airline sought to establish a future-ready data platform capable of supporting additional business domains and advanced analytics initiatives.

Scope of Services

Platform Modernization & Migration

  • Re-engineered the legacy commercial domain DWH into an AWS-native architecture

  • Built a scalable Data Lake using S3, Redshift, Spark, Hive, and NiFi

  • Migrated complex data pipelines while resolving functional and process interdependencies

End-to-End Reference Architecture

  • Designed a cloud-first architecture optimized for analytics, storage, and compute

  • Implemented modular processing layers for ingestion, transformation, and data consumption

Functional Review & Business Rule Redesign

  • Conducted functional assessment across pricing, revenue, sales, and marketing processes

  • Rationalized and redesigned business rules to eliminate redundancies

  • Standardized KPI definitions across commercial units

Governance & Quality Framework

  • Implemented data quality, metadata management, and lineage tracking

  • Established governance workflows and role-based data access

Reporting & Insights Enablement

  • Enabled self-service analytics and reporting for commercial teams

  • Consolidated insights across pricing, demand, marketing, and revenue domains

Benefits

  • Significant cost savings through consolidation of technology infrastructure

  • Simplified reporting environment enabling faster insights

  • Reduced dependency on IT teams through self-service analytics

  • Improved data quality and governance through enterprise frameworks

  • Streamlined business rules eliminating complex interdependencies

  • Unified commercial data repository supporting cross-functional analytics

  • Flexible platform capable of onboarding new business domains

Impact

  • 40–60% reduction in operational overhead after eliminating legacy systems

  • Faster insight generation through self-service access for revenue and pricing teams

  • Improved commercial decision accuracy through standardized KPIs

  • Analytics project lead time reduced from weeks to days

  • Future-ready data platform enabling AI and machine learning use cases

Introduction

Cloud Data Platform Modernization enables healthcare organizations to unlock the full value of enterprise data by replacing fragmented legacy systems with scalable and unified platforms. Many healthcare groups operate multiple operating companies (OpCos), each maintaining separate data repositories. As a result, data becomes siloed, analytics initiatives slow down, and enterprise-wide insights become difficult to generate.

This case study highlights how an APAC-based healthcare group modernized its data landscape through Cloud Data Platform Modernization. By migrating legacy on-premise platforms to the cloud and consolidating fragmented OpCo-level repositories, the organization established a scalable data foundation. Consequently, the healthcare group improved operational efficiency, enabled advanced analytics, and strengthened its readiness for future AI-driven innovation.

Customer

The customer is an APAC-based healthcare group operating multiple operating companies (OpCos) across the region. Each OpCo maintained independent data repositories and infrastructure, which created fragmentation across the enterprise data environment.

As the organization expanded, these siloed systems limited analytics capabilities and slowed down enterprise data initiatives. Therefore, the healthcare group required a modern cloud-based platform capable of supporting unified analytics and scalable data operations.

Business Objective

The organization aimed to modernize legacy on-premise data platforms while consolidating fragmented OpCo-level repositories into a unified environment. In addition, leadership sought to enable cloud scalability and improve operational efficiency across the enterprise.

Another key objective was to support data monetization and advanced analytics use cases while securely integrating third-party data sources. At the same time, the platform needed to maintain 24×7 operational reliability and support business-critical healthcare operations.

Scope of Services

The engagement focused on end-to-end data platform transformation, including:

  • Migration of on-premise data platforms to the cloud

  • Consolidation of multiple OpCo data repositories into a unified platform

  • Design and implementation of scalable cloud-based data architecture

  • Development of data monetization and analytics use cases

  • Integration of third-party data sources

  • Ongoing 24×7 platform support and maintenance

Benefits

  • Unified and scalable data platform across operating companies

  • Improved data accessibility and analytics capabilities

  • Faster development of data monetization use cases

  • Reduced complexity caused by fragmented data repositories

  • Reliable round-the-clock platform operations

  • Strong foundation for advanced analytics and future AI initiatives

Impact

  • Accelerated data-driven decision-making across operating companies

  • Improved operational efficiency across the healthcare group

  • Increased value realization from enterprise data assets

  • Enhanced readiness for advanced analytics and AI adoption

Introduction

Digital Engineering Transformation enables product-driven enterprises to improve R&D efficiency, reduce engineering costs, and accelerate innovation across complex product portfolios. In large-scale product engineering organizations, fragmented testing environments, growing software backlogs, and rising maintenance costs limit productivity and slow time-to-market.

This case study highlights how Digital Engineering Transformation helped a multinational digital printing and document management corporation consolidate engineering operations across software, hardware, and mechanical domains. By standardizing testing frameworks, improving integration workflows, and optimizing engineering structures, the organization reduced costs, improved quality, and extended product lifecycle performance. As a result, engineering shifted from reactive maintenance to structured, innovation-led delivery.

Customer

The customer is a well-known American multinational corporation specializing in digital printing, document management, and business services. The organization operates globally with a large-scale product engineering workforce supporting printer portfolios and digital systems.

With growing product complexity and global scale, the engineering organization required a more efficient and standardized approach to testing, defect management, and lifecycle optimization.

Business Objective

The primary objective was to achieve significant cost savings through engineering efficiency and consolidation. At the same time, the organization aimed to reduce the backlog of software and system defects.

Leadership also sought to lower product run and maintenance costs while extending product lifecycle across printer portfolios. Additionally, the company needed to accelerate time-to-market for new features without increasing R&D spending.

Ultimately, the goal was to improve engineering productivity while maintaining innovation velocity and product quality.

Scope of Services

The engagement focused on end-to-end digital engineering and testing across software, hardware, and mechanical domains.

Software development testing covered C, C++, Java, Unix, and Solaris technology stacks. Hardware development testing included Cricut systems, PCB, FPGA, and ASIC components. Mechanical engineering testing addressed design drawings, 3D CAD models, and FMEA processes.

System integration testing ensured coordinated product design and feature development across domains.

Finally, engineering consolidation initiatives improved scalability, standardized quality processes, and increased delivery efficiency across the global product portfolio.

Benefits

  • Improved engineering efficiency through consolidation and standardization

  • Faster defect resolution and reduced software backlog

  • Lower product maintenance and operational costs

  • Extended product lifecycle across printer families

  • Higher R&D throughput without increased engineering spend

  • Faster delivery of product features

  • Sustained innovation supported by Agile delivery models

Impact

  • $100M+ cost savings for the engineering organization

  • 30–40% reduction in software backlog

  • 20% reduction in product run and maintenance costs

  • 10–25% extension in product lifecycle

  • 30% faster time-to-market

  • $700M+ overall business impact

  • 400 innovation disclosures filed

  • 345 patents filed

  • 1,200 engineers engaged

  • 100,000 sq. ft. lab space and 30 Scrum rooms supporting Agile delivery

Introduction

AI-Driven Performance Management Automation enables enterprise organizations to modernize employee evaluation processes while improving feedback quality and manager experience. In many enterprises, performance evaluations involve repetitive manual steps, inconsistent feedback standards, and time-consuming administrative work. As a result, managers experience evaluation fatigue, and employees receive variable feedback quality.

To address these challenges, the organization adopted AI-Driven Performance Management Automation to simplify evaluation cycles and enhance engagement. Instead of relying solely on static forms and manual workflows, managers now interact through conversational, intelligent interfaces. Consequently, evaluation processes became faster, more structured, and more scalable. At the same time, employee satisfaction improved due to more consistent and thoughtful feedback.

Customer

The customer is an enterprise organization focused on strengthening employee performance management across teams. The organization sought to simplify performance evaluations while maintaining high-quality feedback standards.

However, repetitive processes and inconsistent evaluation approaches reduced efficiency and negatively impacted employee Net Promoter Scores (NPS). Therefore, the organization required a scalable and flexible solution that could improve manager experience while maintaining structured governance.

Business Objective

The primary objective was to reduce the repetitive and time-consuming nature of performance evaluations. Additionally, leadership aimed to improve the quality and consistency of employee feedback across teams.

The organization also sought to enhance the manager experience during evaluation cycles. At the same time, it wanted to prevent declines in employee NPS caused by poor evaluation experiences. Ultimately, the goal was to enable flexible and scalable performance management interactions without increasing HR overhead.

Scope of Service

BXI delivered an AI agent–based performance management solution designed to modernize evaluation workflows.

First, the team designed and deployed a conversational, non–rule-based NLP chatbot that functioned as a virtual HR recruiter. Unlike traditional static systems, this chatbot supported dynamic and context-aware interactions.

Next, BXI developed secure APIs to authenticate and authorize access to the performance management system. The solution enabled real-time querying and updating of performance data through conversational workflows.

In addition, NLP and intent recognition capabilities in English ensured accurate understanding of manager inputs. Finally, seamless integration between the chatbot and the performance management platform ensured smooth data synchronization and process continuity.

Benefits

  • Reduced effort and time spent by managers on performance evaluations

  • Improved feedback quality through guided conversational interactions

  • Higher efficiency across evaluation cycles

  • Enhanced employee experience driven by more structured and timely feedback

  • Flexible, anytime-anywhere evaluation process improving adoption

Impact

  • Faster completion of performance reviews

  • Improved consistency and depth of employee feedback

  • Increased manager and employee satisfaction

  • Scalable performance management without additional HR overhead

Introduction

Legacy parcel systems often limit scalability, visibility, and customer experience—especially during peak demand. This case study highlights how Parcel Digitization and Cloud Modernization for Retail Logistics enabled a multinational retailer to replace fragmented legacy workflows with a real-time, event-driven parcel ecosystem, improving performance, resilience, and customer satisfaction.

Customer

A multinational retailer operating large-scale parcel and delivery networks faced severe scalability and performance challenges due to legacy, fragmented systems. High operational costs, frequent outages during peak demand, and slow response to market needs directly impacted customer experience and revenue growth.

Business Objectives

The retailer initiated a Parcel Digitization and Cloud Modernization program to stabilize operations and enable future growth. Key objectives included:

  • Modernize IT infrastructure for agility and reliability

  • Reduce maintenance and infrastructure scaling costs

  • Improve system performance to enhance customer experience

  • Enable rapid market expansion and digital innovation

  • Transition to a resilient, scalable cloud-based environment

Scope of Services

BXI Technologies partnered with the client to transform the end-to-end parcel ecosystem across digital, integration, and operational layers.

Parcel Digitization

  • Enabled end-to-end digital capture for every parcel event (scan → sort → route → deliver)

  • Replaced legacy workflows with real-time, event-driven digital processes

Integration Modernization

  • Modernized the enterprise-wide integration landscape

  • Built unified integration across Parcel Systems, Sortation Hubs, and Route Planning Systems

  • Implemented an enterprise-grade event-driven architecture

Customer Experience Transformation

  • Introduced real-time in-flight delivery change capability

  • Enabled doorstep parcel collection and enhanced task assignment

  • Improved track-and-trace visibility and customer notifications

Operational Intelligence & Monitoring

  • Deployed comprehensive monitoring, alerting, and observability tools

  • Implemented Solution Manager, WILY, HAWK alerting, and EEM

  • Enabled end-user experience analytics and proactive issue detection

Benefits

  • 100% Parcel Digitization enabling real-time tracking, notifications, and billing

  • In-Flight Delivery Change Capability allowing customers to modify deliveries mid-transit

  • Enhanced Operational Efficiency through task automation and PDA-driven interventions

  • Optimized Sortation and Routing using EPS-driven event intelligence

  • 60% Reduction in Incident Volume through Smart Rules automation

Impact

  • Achieved end-to-end visibility across the entire parcel journey

  • Significantly improved customer experience, increasing onboarding and retention

  • Positioned the retailer competitively against digital-first logistics providers

  • Reduced operational overhead and error rates across sortation and last-mile delivery

  • Enhanced real-time decision-making for sortation teams

  • Established an enterprise integration backbone to support future innovation

Introduction

Retail and consumer goods enterprises face increasing pressure to respond faster to demand volatility, supply chain disruptions, and omnichannel complexity. This case study highlights how AI Enablement Across the Retail Value Chain helped a global Retail & Consumer Goods enterprise embed intelligence across manufacturing, supply chain, and enterprise decision-making—improving agility, resilience, and business outcomes at scale.

Customer

A global Retail & Consumer Goods enterprise operating across complex omnichannel ecosystems, spanning manufacturing, supply chain operations, and enterprise planning. The organization sought to move beyond fragmented analytics and embed AI-driven intelligence across its value chain to support faster, more accurate, and resilient decision-making.

Business Objectives

The customer launched an AI Enablement Across the Retail Value Chain initiative to operationalize intelligence from factory floor to boardroom. Key objectives included:

  • Embed AI-driven intelligence across retail and consumer goods operations

  • Unify fragmented operational, supply chain, and enterprise data

  • Improve demand forecasting and inventory planning accuracy

  • Reduce supply chain inefficiencies and logistics costs

  • Enable leadership with real-time, decision-ready insights

  • Deliver scalable, compliant, and sustainable AI innovation across omnichannel operations

Scope of Services

BXITech delivered an end-to-end AI enablement program spanning data, analytics, and operational intelligence.

Unified Enterprise Data Foundation

  • Integrated factory, supply chain, and commercial data into a single enterprise data layer

  • Eliminated data silos across manufacturing, logistics, and retail operations

AI-Driven Demand & Inventory Intelligence

  • Developed AI models for demand forecasting and inventory optimization

  • Improved production, replenishment, and allocation decisions across regions

Predictive Supply Chain & Logistics Analytics

  • Enabled predictive analytics for stock movement, replenishment, and logistics planning

  • Reduced inefficiencies through proactive exception detection and planning

End-to-End Supply Chain Visibility

  • Delivered regional and enterprise-level visibility into supply chain performance

  • Enabled exception management across suppliers, warehouses, and distribution networks

Omnichannel Intelligence

  • Aligned demand, supply, and customer behavior across digital and physical channels

  • Improved responsiveness to demand shifts and channel-level variability

Governance, Compliance & Responsible AI

  • Implemented model governance, compliance controls, and lifecycle management

  • Ensured scalable, auditable, and responsible AI adoption

Executive Decision Enablement

  • Enabled leadership with boardroom-ready insights spanning operations, supply chain, and financial impact

  • Supported faster, data-driven decision-making at enterprise scale

Benefits Delivered

  • Reduced inventory imbalance by minimizing stockouts and excess inventory

  • Improved forecast accuracy supporting better production and replenishment decisions

  • Lower logistics and supply chain costs through predictive optimization

  • Enhanced working capital efficiency by reducing overstocking and markdowns

  • Stronger margin performance through improved demand–supply alignment

  • Faster, data-driven decision-making across operational and leadership teams

  • Scalable AI adoption with built-in governance and compliance

Impact

  • 15% reduction in stockouts and excess inventory

  • 20% increase in demand forecast precision

  • 10% reduction in logistics and supply chain costs

  • Improved working capital efficiency

  • Lower markdowns and higher margin realization

Introduction

Preventive healthcare and wellness organizations operate under intense regulatory scrutiny while facing pressure to innovate faster and deliver measurable outcomes. This case study highlights how AI-Driven Intelligence for Preventive Healthcare enabled a regulated healthcare and wellness enterprise to accelerate formulation and trial planning, reduce quality incidents, and improve long-term ROI—without compromising compliance, trust, or ESG commitments.

Customer Overview

A healthcare and wellness enterprise focused on preventive care, operating in a highly regulated environment. The organization sought to responsibly embed AI across formulation, trials, quality, and compliance functions to improve efficiency, outcomes, and brand credibility while maintaining regulatory trust.

Business Objectives

The customer launched an AI-Driven Intelligence for Preventive Healthcare initiative with the following goals:

  • Embed AI-driven intelligence into preventive care and wellness operations

  • Reduce formulation and product development cycle times

  • Accelerate clinical and trial planning decisions

  • Minimize quality incidents through proactive exception handling

  • Ensure regulatory compliance, data trust, and ESG alignment

  • Improve ROI and brand credibility through predictive, data-driven outcomes

Scope of Services

BXITech delivered a tailored AI-driven intelligence and exception handling solution designed specifically for regulated healthcare and wellness environments.

Unified Healthcare Data Foundation

  • Integrated data across formulation, trials, quality, and compliance systems

  • Eliminated silos while maintaining data governance and traceability

AI-Driven Exception Detection

  • Implemented AI models to proactively detect risks, deviations, and inefficiencies

  • Enabled early intervention before issues escalated into quality or compliance incidents

Predictive Analytics for Formulation & Trials

  • Applied predictive analytics to accelerate formulation cycles

  • Improved speed and accuracy of trial planning and execution decisions

Quality Intelligence

  • Enabled continuous quality monitoring and adherence to defined processes

  • Reduced quality incidents through proactive, insight-driven actions

Governance, Compliance & Responsible AI

  • Established AI governance and compliance frameworks

  • Ensured regulatory alignment, data trust, and ESG accountability across AI models

Leadership Insight Enablement

  • Enabled leadership with real-time visibility into outcomes, risks, and strategic expectations

  • Supported confident, compliant, and forward-looking decision-making

Benefits

  • Faster formulation cycles through AI-driven insights and exception management

  • Improved speed and accuracy in trial planning and execution

  • Reduced quality risks by identifying issues before escalation

  • Higher ROI through predictive success and optimized resource utilization

  • Increased trust in data, compliance, and ESG outcomes

  • Stronger brand engagement driven by reliable preventive-care innovation

Impact

  • 30% reduction in formulation cycle time

  • 20% faster trial planning decisions

  • 15% fewer quality incidents

  • Improved ROI, predictive success, and ESG-aligned trust metrics

Introduction

Investment firms managing global financial indexes require speed, accuracy, and consistency across geographies to remain competitive. This case study highlights how Global Index Analytics Platform Modernization enabled a US-based investment firm to unify index operations, analytics, and delivery across regions—accelerating time-to-market for new index-based investment products while reducing cost and operational friction.

Customer

A US-based investment firm managing global financial indexes across multiple geographies, including the United States and the United Kingdom. The organization required a unified operating and analytics model to support faster product innovation, consistent reporting, and scalable index services worldwide.

Business Objectives

The customer launched a Global Index Analytics Platform Modernization initiative to standardize and scale index operations. Key objectives included:

  • Create a unified system for US and global (UK) index operations

  • Establish a scalable platform for future index service enhancements

  • Consolidate business processes, reporting, and analytics across indexes

  • Enable faster time-to-market for new investment and index-based products

  • Improve decision-making through consistent, trusted analytics

Scope of Services

BXITech delivered a unified analytics and delivery framework to support global index operations and continuous product innovation.

Unified Analytics & Reporting Platform

  • Designed and implemented a single analytics and reporting platform for US and UK index operations

  • Enabled consistent data access, reporting standards, and analytical views across regions

Process & Data Consolidation

  • Consolidated index-related business processes and data workflows

  • Reduced operational fragmentation across index services

Global Agile Delivery Model

  • Implemented an agile delivery model operating across three time zones (US, UK, India)

  • Core and requirements teams based in the US and UK

  • Four delivery teams based in India supporting continuous development

Continuous Delivery & Automation

  • Established a Continuous Delivery framework enabling frequent, reliable releases

  • Implemented automated testing across unit, integration, and regression layers

Collaboration & Execution Excellence

  • Adopted high-collaboration agile practices:

    • Daily standups

    • Scrum-of-scrums

    • Sprint planning and reviews

  • Enabled real-time collaboration using audio/video conferencing, digital whiteboards, and on-demand requirement clarification

Benefits

  • Faster and more reliable user acceptance through improved automation and quality controls

  • Higher code quality and consistency across global delivery teams

  • Increased deployment frequency enabling rapid iteration

  • Faster launch of new index-based investment products

  • Significant cost reduction through delivery optimization and automation

  • Improved collaboration and alignment across distributed teams

  • Long-term partnership driven by delivery consistency and business impact

Impact

  • 83% reduction in UAT effort

  • 80% code quality coverage

  • 5 deployments per day (2 Dev, 2 SIT, 1 UAT)

  • 55% reduction in overall delivery cost

  • 4+ years of sustained engagement and partnership