Automated app and infra deployment with DevOps implementation help a leading medical company launch applications in new geographies, improve time-to-market, and reduce the total cost of ownership.
Client
North America-based fertility and genomics company
Goal
Expand business reach, reduce time-to-market, and support critical compliance
Tools and Technologies
.NET 5, Vue.js, AWS Secrets Manager, AWS Transfer Family, Amazon RDS, Amazon EKS, Amazon Route 53, Amazon CloudFront, Terraform, GitLab
Business Challenge
The client wanted to expand its reach to Canada, Europe, and APAC regions to meet the requirements for a 10x increase in their user base. Legacy application infrastructure and code built on the old tech stack, with high technical debt, were slowing down the rollout of new features, making the client less competitive. The infra-deployment process was only partially automated, stretching the time-to-market to three months. The total cost of ownership was relatively high. HIPPA and PII compliance were also not supported.
Solution
Iris modernized the application into microservices, built the infrastructure using Terraform and automated its provisioning and configuration.
- Application developed using .NET 5 and Vue.js
- Architecture transformed into cloud-native
- AWS Managed Services, including Secrets Manager, AWS Transfer Family, RDS, EKS, Route 53, CloudFront, and S3, configured using Terraform
- EKS Cluster and associated components provisioned via Terraform
- App pushed to container registry using GitLab pipeline
- Secrets (API keys, database connection strings, etc.) and app images moved to EKS Cluster using S3 Bucket Helm
- Static code analysis, coverage and vulnerability scans integrated to ensure code quality and reduce configuration issues
Outcomes
Our DevOps solution enabled the client to achieve significant benefits, including:
- Application launch in Canada and Europe; Asia Pacific release in the pipeline
- HIPPA and PII compliance
- 5x scalability improvement from weekly average usage
- Time-to-market reduced from three months to 3 weeks
- Total cost of ownership lowered by 50%
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Order management platform transformation
Platform transformation and multi-cloud integration improve multinational publishing company's order management, time-to-market and performance.
Client
Multinational publishing, media, and educational company
Goal
Improve order management and transaction processing capabilities
Technology Tools
AWS EKS, Kong, Salesforce Commerce Cloud (SFCC), Salesforce CRM, Jenkins, Sumo Logic, Datadog
Business Challenge
The client's order management platform was complex and had scalability issues, causing poor customer experience and loss of revenue. The platform was hosted on Oracle cloud, with data stored in different repositories. Services were also hosted in the Oracle cloud, which used the BICC extract to fetch information about order details from Oracle databases. The low performance of customer-facing applications was causing latency and very high transaction processing time.
Solution
Team Iris transformed Oracle-based SOA services into six microservices and migrated them to AWS EKS for autoscaling with self-healing and monitoring capabilities.
We developed services for publishing data to Salesforce CRM for quick order processing and conversions. The BICC system for diversified information and order history was enabled with real-time integration between Oracle Fusion and materialized views for data consumption.
Post migration, these services were registered in Kong for discovery, and a CI/CD pipeline was created for deployment using Jenkins. Sumo Logic was used for monitoring the logs, and Datadog was used to observe latency, anomalies and other metrics.
Outcomes
The order management platform transformation delivered the following benefits to the client:
- System performance improved by 70%
- Transaction processing capability increased by 4x
- Order processing capabilities were enhanced by 200%
- Total cost of ownership (TCO) was reduced by 30%
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API migration benefits leading logistics company
Phased migration of Boomi APIs to Apigee helps a leading logistics company improve performance and scale without business disruption.
Client
A leader in truck transportation and logistics services
Goal
Migrate Boomi-based logistics APIs to improve performance and scalability
Technology Tools
Apigee, Boomi, Swagger, JMeter, Postman, GCP
Business Challenge
The client's existing Boomi Atom platform with logistics APIs had lifecycle and monitoring issues, with frequent and elongated downtimes, causing customer experience challenges.
The system did not support the logging of events, and API transactions were untraceable. Identifying the number of customers facing issues and incidents when APIs were not working was difficult. The absence of alerting mechanisms, scalability concerns, and the Boomi platform's high licensing costs were other critical challenges.
An optimized API governance system was required to provide an abstraction for the backend services, security, and efficiencies around rate limiting, quotas, and analytics.
Solution
Iris strategized the smooth transition of 250+ Boomi APIs, starting with 20 in the pilot phase. The entire migration was planned to occur in four waves.
First, the pseudocode of Boomi APIs was documented and reviewed. The team then developed proxies in Apigee X following a TDD (Test-driven Development) approach. A well-defined logging framework was provided to the client for capturing appropriate parameters for tracking API calls.
Seamless migration of API keys from Boomi API Management (APIM) to Apigee X apps was performed. Network routing at F5 for the individual proxies was implemented to transfer the traffic from Boomi to Apigee post migration in each wave. Process metering, monitoring, and adherence/compliance hooks were inserted into the system.
Outcomes
Our API migration solution delivered the following outcomes:
- Improved performance and scalability by 30%
- Centralized logging and alerting for both APIM and backend systems resulting in 40% MTTR (Mean Time for Ticket Resolution)
- Apigee analytics enablement for API traffic, request latency, response time, target errors, and transaction revenue analysis
- Enablement of API discovery, monetization, registration, partner onboarding, and governance
- Ability to integrate the system into the Apigee developer portal
- Eliminated Boomi licensing cost
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Custom analytics enable faster business decisions
Optimized data and on-demand analytics deliver faster business insights and better user experience for asset management firm.
Client
U.S.-based asset management company
Goal
Streamline and improve data and analytics capabilities for enhanced user experiences
Technology Tools
Java, React JS, MS SQL Server, Spring Boot, GitHub, Jenkins
Business Challenge
The client captures voluminous data from multiple internal and external sources. The absence of quick, on-demand capabilities for business users was inefficient in generating customized portfolio analytics on attributes such as average quality, yield to maturity, average coupon, etc.
The client teams were spending enormous amounts of manual effort and elapsed time (approximately 12-15 hours) to respond to requests for proposals from their respective clients.
Solution
Iris implemented a data acquisition and analytics system with pre-processing capabilities for grouping, classifying, and handling historical data.
A data dictionary was established for key concepts, such as asset classes and industry classifications, enabling end users to access data for analytical computation. The analytics engine was refactored, optimized, and integrated into the streamlined investment performance data infrastructure.
The team developed an interactive self-service capability, allowing business users to track data availability, perform advanced searches, generate custom analytics, visualize information, and utilize the insights for decision-making.
Outcomes
The solution brought several benefits to the client, including:
- Simplified data access to generate custom analytics for end users
- Eliminated manual processing and the need for complex queries
- Enhanced the stakeholder experience
- Reduced response time to client RFPs by over 50%
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Next-gen platform reliability engineering for Blockchain-DLT
Banking & Financial Services
Next-gen platform reliability engineering for Blockchain-DLT
Reliability engineering helps a digital financial services client smoothly migrate its legacy Blockchain-DLT (Digital Ledger Technology) platform and gain advanced automation coverage and patch delivery efficiencies.
Client
A leading digital financial services company
Goal
Blockchain- DLT platform assurance with improved automation coverage
Tools and Technologies
Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Services (AKS), Docker, Terraform, Helm Charts, Microservices, Kotlin, Xray
Business Challenge
The client's legacy DLT platform did not support cloud capabilities with the Blockchain-DLT tech stack. The non-GUI (Graphic User Interface) and CLI (Command Line Interface)-based platform lacked the microservices architecture and cluster resilience.
The REST (Representational State Transfer) APIs-based platform did not support platform assurance validation at the backend. Automation coverage for legacy and newer versions of the products was very low. Support for delivery patches was insufficient, impacting the delivery of multiple versions of R3 products each month.
Solution
Iris developed multiple CorDapps to support automation around DLT-platform functionalities and enhanced the CLI-based & cluster utilities in the existing R3 automation framework.
The team implemented the test case management tool Xray to improve test automation coverage for legacy and newer versions of the Corda platform, enabling smooth and frequent patch deliveries every month.
The quality engineering process was streamlined for the team's Kanban board by modifying the workflows. Iris also introduced the ability to execute a testing suite that could run on a daily or as-needed basis for AKS, EKS, and Local MAC/ Windows/ Linux cluster environments.
Outcomes
The Blockchain-DLT reliability assurance solution enabled the client to attain:
- Improved automation coverage of the DLT platform with 900 test cases with a pass rate of 96% in daily runs
- Compatibility across AWS-EKS, Azure-AKS, Mac, Windows, Linux, and local clusters
- Increased efficiency in deliverables with an annual $35K savings in the test case management area
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Big Data platform improves global AML compliance
A multinational bank leverages big data platform to improve Anti Money Laundering (AML) compliance and protect global clients and franchises from financial crimes.
Client
A leading global bank with operations in over 100 countries
Goal
Address data quality and cost challenges of legacy AML application infrastructure
Tools and Technologies
Hadoop, Hive, Talend, Kafka, Spark, ETL
Business Challenge
The client’s legacy AML application infrastructure was leading to data acquisition, quality assurance, data processing, AML rules management and reporting challenges.
High data volume and rules-based algorithms were generating high numbers of false positives. Multiple instances of legacy vendor platforms were also adding to cost and complexity.
Solution
Iris developed and implemented multiple AML Trade Surveillance applications and Big Data capabilities. The team designed a centralized data hub with Cloudera Hadoop for AML business processes and migrated application data to the big data analytical platform in the client’s private cloud. Switching from a rule-based approach to algorithmic analytical models, we incorporated a data lake with logical layers and developed a metadata-driven data quality monitoring solution.
We enabled the support for AML model development, execution and testing/validation, and integration with case management. Our data experts also deployed a custom metadata management tool and UI to manage data quality. Data visualization and dashboards were implemented for alerts, monitoring performance, and tracking money laundering activities.
Outcomes
The implemented solution delivered tangible outcomes, including:
- Centralized data hub capable of handling 100+ PB of data and ~5,000 users across 18 regional hubs for several countries
- Ingestion of 30+ million transactions per day from different sources
- Greater insights with scanning of 1.5+ Billion transactions every month
- False positives reduced by over 30%
- AML data storage cost reduced to <10 cents per GB per year
- Extended support to multiple countries and business lines across six global regions; legacy instances reduced from 30+ to <10
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Investment warehouse enhances communications
Account management and marketing teams of an investment bank acquired improved multi-channel client communications and portfolio management capabilities with a new data infrastructure and a single source of truth.
Client
A U.S.-based investment bank
Goal
Improve data collation and information quality for enhanced marketing and client reporting functions
Tools and Technologies
Composite C1, Oracle DB, PostgreSQL, Vermilion Reporting Suite, Python, MS SQL Server, React.js
Business Challenge
The client’s existing investment data structure lacked a single source of truth for investment and performance data. The account management and marketing teams were making significant manual efforts to track portfolio performance, identify opportunities and ensure accurate client reporting. The time-consuming and manual processes of generating marketing exhibits and client reports were highly error-prone.
Solution
Iris implemented a comprehensive investment data infrastructure for a single source of truth and improved reporting capabilities for marketing content and client report generation.
An automated Quality Assurance process was instituted to validate the information in critical marketing materials, such as fact sheets, snapshots, sales kits, and flyers, against the respective data source systems.
Retail and institutional portals were developed to provide a consolidated view of portfolios, with the ability to drill down to underlying assets, AUM (Assets Under Management) trends, incentives, commissions, and active opportunities.
Outcomes
The new data infrastructure delivered a holistic, on-demand view of investment details, including performance characteristics, breakdowns, attributions, and holdings, to the client's marketing team and account managers with:
- ~95% reduction in performance data and exhibit information discrepancies
- ~60% improvement in operational efficiency in core marketing and client reporting functions
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Brokerage platform transformation improves UX
A monolithic registered investment advisor (RIA) platform transformed into microservices helps a leading brokerage firm achieve enhanced operations and user experience.
Client
A leading U.S. brokerage firm with $1+ trillion in assets and serving 6,000+ RIAs
Goal
Resolve online platform accessibility, functionality and timeliness issues
Tools and Technologies
Angular 9, Jenkins, Pivotal Cloud Foundry, Oracle, Kubernetes, Spring, Docker
Business Challenge
Client’s existing brokerage platform supporting over 6,000 Registered Investment Advisors (RIAs) and containing information about assets valued at more than $1 trillion had accessibility issues. The high cost of owning and maintaining outdated technologies and time-to-market for new features were adding to the business challenges.
Solution
Iris transitioned the client’s monolith applications to microservices to transform the RIA platform. An open-source, cloud technical stack was leveraged to develop a single-page, micro-UI-based application. BFF (Backend for Frontend) design was applied, and Angular 9 was used to achieve superior compatibility on mobile devices.
Widgets were introduced to enable seamless transitions within third-party applications. Consolidated user views were created to track assets and their performance for a unified experience for the RIAs.
Outcomes
The RIA platform transformation enabled the client to achieve significant functional enhancements, including:
- Fully functional mobile views
- 100+ integrated third-party applications
- Instant and seamless access to client accounts
- Downtime for hot deployments of fixes brought to zero
- Technical debt decreased by 45%
- Release timelines shortened by 80%
- Issue resolution time reduced by 90%
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Cloud data lakehouse for single source of truth
Data systems built on legacy technologies were delaying month-end activities and actionable insights for finance and sales teams. Transformation of data and BI applications to MS Azure delivered "Order to Cash" sales analytics on cloud.
Client
A medical devices and fertility solutions company
Goal
Establish a cloud data warehouse for a single source of truth and timely month-end activities
Tools and Technologies
Azure Data Factory, Azure Data Lake, Power BI, Synapse Analytics
Business Challenge
The client had multiple instances of ERPs, sales systems, and warehouses built on obsolete technology and frameworks. The existing system siloed the data, resulting in inconsistent versions of the truth. The client's finance and sales teams were struggling to reconcile data offline and feed the same back into the ERPs, causing significant delays in month-end activities.
As the record systems were also not synced and legacy reports were built on the interim warehouses, line managers and executive teams were not able to extract actionable and comprehensive insights. A solution to onboard and integrate new datasets on an ongoing basis was required to support the data merger and acquisition process.
Solution
A strategy to transform data and BI applications to MS Azure was finalized. The transformation was executed in phases and included discovery, report rationalization and foundational build of a global system of reporting.
The solution included the data ingestion process with Azure Data Factory, data storage and processing using Azure Data Lake and Synapse Analytics, reports and dashboards with Power BI.
A utility to accelerate the onboarding of new data entities was conceptualized and delivered to onboard and integrate new datasets to support mergers and acquisitions.
Outcomes
Iris data practitioners helped the client overcome key challenges and advanced data warehousing capabilities by:
- Establishing a "single version of the truth" that enabled data-driven decisions and timely completion of month-end and other critical activities
- Delivering analytics for “Order to Cash” processes, including subject areas of sales, inventory, shipping, finance, etc.
- Facilitating the generation of actionable, insightful reports and dashboards, allowing "self-service" consumption for the business leadership
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Tech stack automation expedites development by 3x
Manual processes across the multi-technology stack were severely affecting the script development cycles in terms of time, effort and cost. Iris application agnostic Test Automation framework and DevOps integration helped the client reduce the script development time and cost significantly.
Client
A leading building supplies manufacturing company
Goal
To support 30+ applications stack for UI, E2E, APIs, performance, mobile automation along with DevOps pipeline integration
Tools and Technologies
.NET Core, PeopleSoft, Salesforce, WMS, JavaScript, Angular, Foxpro, C#, Selenium, SpecFlow, RestSharp, Nunit, Mobile Center/Emulators, Allure, Jira, Azure Pipeline, GitHub
Business Challenge
The client had technology stacks comprising of diverse technologies that were difficult to manage. Substantial manual effort and time were spent on integrating the checkpoints, elongating the development process. Validating end-to-end business flows across different applications was the prime challenge. Reporting processes were also scattered across the entire application stack, making it vulnerable.
Solution
Iris developed a robust application agnostic Test Automation framework to support the client’s multiple-technology stacks. Following the Behavior-driven Development (BDD) approach to align the acceptance criteria with the stakeholders, we built business and application layers of the common utilities in the core framework.
Our experts identified E2E business flows to validate the downstream impact of the change and automated the entire stack through the shift-left approach. Azure DevOps integration enabled a common dashboard for reporting. The client attained complete version control to track production health and enforce strong validations.
Outcomes
Iris Automation solution enabled the client to surpass several business goals. The key outcomes of the delivered solution included:
- ~65% Increase in automation coverage
- 100+ Pipelines for in-scope applications across multiple environments
- 3700+ Test Automation scripts execution per sprint cycle achieved across applications
- 3X Faster script development of behavior-driven test cases
- Multi-day manual test effort reduced to a few hours of automated regression
- 70% Reduction in effort
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