Adroitent

January 2025

Established Quality Assurance Center of Excellence for Automotive Software leader

Adroitent Quality Engineering Services established and set up a Competence-driven Technology Practice – Quality Assurance Center of Excellence(QACoE) for an Automotive Software Leader in the US. Key Business Outcomes Enabled 20-30% cost savings with early detection of defects across SDLC Accelerated time-to-market by 10% Improved customer satisfaction significantly About the Customer The customer is a technology-enabled automotive leader that brings dealers and consumers together across North America with cutting-edge software products for car dealers. The customer’s all-in-one software for dealers includes trade-in valuations, payment calculators, digital brochures, videos for sales teams, service departments, and other service offers. Customer Challenge The customer faced critical challenges with their software as their time-to-market was largely delayed with bugs identified in production and they did not have an effective quality assurance (QA) practice within their system. Their software releases were delayed due to defects and issues identified and had no proper QA in place to effectively verify and validate their software. The existence of issues impacted their dealers and other prospects and adversely affected their business bottom line. Solution Delivered Adroitent partnered with the customer to establish and manage an end-to-end Quality Assurance Center of Excellence (CoE) as a part of establishing a Competence-driven Technology Center of Excellence (TCoE). Adroitent’s team established a centralized hub to standardize, improve, and oversee quality practices across the customer’s projects to ensure best quality assurance practices are adopted to enable high-quality solutions. Solution Highlights Adroitent’s quality assurance (QA) team established a Quality Assurance Center of Excellence by setting up a testing practice that helped the customer to continuously improve its quality assurance process, reduce defects, and increase the quality of deliverables, thus achieving quicker time-to-market and quicker ROI. Technology Leveraged The technology leveraged included JIRA, Playwright test automation tool, and X-ray for test management. Activities taken up as Part of establishing Quality Assurance Center of Excellence (CoE) Deployed Adroitent’s core team: The Adroitent’s core team involved in this project consisted of the Delivery Head, Project Manager, Test Lead, Test Engineers (Manual and Automated), and Business Analyst. Leveraged Automation tools and technologies: Adroitent’s QA teams leveraged the technology stack that included Typescript – Playwright automation tool, Jira, X-ray for test management, and Confluence to ensure seamless testing of the modules. Established reusable test document repository: QA teams enabled effective documentation, developed test plans and procedures, and adopted best QA practices. The reusable test assets created were test cases, automation scripts, and various other test templates to ensure quicker and faster testing outcomes for the customer. Developed a comprehensive test automation framework: The process included Test planning and design, Test case creation and maintenance, Test execution and reporting, and Test data management. Adroitent’s QA teams adopted some key considerations for automation testing that included prioritization of test case selection, effective test data management, Test environment setup, Test reporting and analysis along with effective maintenance and optimization.  Established scalable quality assurance processes: Enabled functional testing for the customer. We performed reviews to ensure a seamless quality assurance process. Teams performed Defect Management to identify processes for identifying, tracking, and resolving defects. Enabled seamless reporting and analytics: Teams were involved in reporting dashboards and also enabled real-time views of quality metrics, defect trends, and test coverage. The detailed reports on quality performance were shared with the product owner and stakeholders and provided effective data-driven recommendations for QA process improvements. Adopted a process of continuous improvement: QA teams regularly assessed the CoE’s performance and identified opportunities for improvement. Teams ensured to stay updated with the latest testing trends and technologies to enable effective outcomes for the customer. Adroitent also conducted regular training and skill development programs for its team members. Business Outcome Teams identified defects at an early stage and the %Defect Rate on average was 31% thus reducing the overall costs by 20-30%. Production defect slippage was reduced significantly for critical and high-severity defects. Ensured better test coverage: QA teams created more test cases to ensure superior and better test coverage. This led to fewer defects and avoided repetitive work at later stages, ensuring enhanced customer satisfaction with a fully functional product. Enhanced efficiency and overall productivity: QA CoE helped to streamline processes, workflows, and resources, which enhanced efficiency and overall productivity. The customer had fewer production issues and other quality issues, leading to lower costs, and higher productivity, helping them gain a better market position. Improved customer satisfaction: QA team identified tricky bugs in mobile apps and effective use cases were written and tested for both iOS and Android. The QA team approach focused more on Smoke tests which helped to find more bugs apart from the tickets and supported infra execution through automated test cases that improved customer satisfaction significantly. Accelerated time-to-market by 10%: Adroitent’s QA CoE establishment helped to provide high-quality products to market by identifying and resolving show-stopper issues at an early stage during the agile development, thus accelerating time-to-market by 10%. Reduced costs and risks: QA CoE teams helped the customer to minimize the defects and ensured no re-work which reduced costs and risks significantly. Connect with us

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Helped a Healthcare Wellness Provider With Analytics Platform Using Ruby on Rails

Ruby on Rails Health Analytics Platform | Adroitent Home/ Customer Stories/ Software Engineering Customer Story · Software Engineering Predicting health: Ruby on Rails at wellness scale How Adroitent built a highly configurable predictive health analytics platform for a wellness provider — plugin-based Ruby on Rails, integrations with labs and diagnostic centers, and AI-driven health projections. Adroitent Case FileHealthcare WellnessRuby on Rails · AWS3 min read The inflection pointHealth data that needed to predict, not just store The customer is a healthcare wellness provider offering predictive analytics to prevent and manage chronic diseases in the early stages — analyzing users’ uploaded diagnostic reports to predict risk of chronic disease and suggesting wellness programs. Needed a comprehensive solution enabling predictive health management Required seamless integration with different service providers and diagnostic labs Demanded a scalable and flexible solution across multiple healthcare systems Health data required strict security and compliance handling Turn diagnostic reports into early, actionable prediction. The interventionA configurable, plugin-based platform Adroitent developed a highly configurable healthcare platform on Ruby on Rails, architected for scalability, easy customization and deep integration across the healthcare ecosystem. Plugin-based architecture A highly configurable platform built on a plugin-based architecture Ensures scalability and allows easy customization per organization’s requirements Multi-provider integrations Connects users to a wide range of labs, diagnostics centers and healthcare providers Flexibility for users to choose the services that best meet their needs User-friendly interface Clear, color-coded test results with graphical history Clickable timelines so users can track health trends over time Secure cloud architecture Hosted on AWS private cloud with ISO 27001 information security standards High availability with multi-AZ disaster recovery; DMZ, VPC and intrusion prevention The game changer AI-driven predictive health projections Advanced AI algorithms analyze health data with digitization Organizations can measure employee health status and identify patterns or trends Patients view their health trends projected graphically and clearly Predictive data identifies areas for targeted healthcare initiatives Ruby on RailsPythonReact.jsPostgresAWSHeroku By the numbers Targeted healthcare initiatives Proactive health management Holistic organizational view Clear health projections By the numbers Targeted healthcare initiatives Proactive health management Holistic organizational view Clear health projections The payoffProactive, preventive, personal Helps implement targeted healthcare initiatives An organization’s predictive data can be used to identify areas for improvement and implement targeted healthcare initiatives for employees, offering a holistic view of organizational well-being. Enables proactive management The platform uses advanced AI algorithms with digitization to analyze health data, enabling organizations to measure employee health status and identify patterns or trends for proactive management. Generates health projections for patients Patients can view and understand trends in their health patterns, with values projected graphically and easily understood. Scalable and secure by design A plugin-based architecture on AWS private cloud with ISO 27001 standards, multi-AZ disaster recovery and intrusion prevention ensured scale without compromising security. The stack behind itTools & technology Ruby on RailsPythonReact.jsjQueryAndroid / iOSPostgresAWSElasticacheNGINXHeroku This engagement was delivered as part of Adroitent’s Software Engineering Services practice. Good to knowFrequently asked questions What is Ruby on Rails best suited for? Ruby on Rails is a mature web framework favoured for rapid development of database-backed applications. Its conventions, mature ecosystem and productivity make it well suited to platforms, marketplaces and data-driven products that need to evolve quickly. What is a plugin-based architecture? A plugin-based architecture builds a core system that can be extended through self-contained modules. New capabilities or client-specific customizations are added as plugins, so the platform scales and adapts without rewriting or destabilizing the core. What is predictive analytics in healthcare? Predictive analytics applies statistical models and machine learning to health data — such as diagnostic reports and history — to estimate future risk, like the likelihood of developing a chronic condition, so that preventive action can be taken earlier. How is health data secured in cloud applications? Through layered controls: private cloud hosting, encryption in transit and at rest, network isolation using DMZ and VPC, intrusion prevention, access controls, and adherence to standards such as ISO 27001 — plus high availability and disaster recovery. Why integrate a health platform with labs and diagnostic centers? Direct integration lets results flow into the platform automatically, giving users a complete longitudinal record without manual entry. It improves data accuracy and timeliness, and gives users the flexibility to choose the providers that suit them. Build platforms that predict, not just report. Scalable product engineering for data-driven healthcare. Start a conversation Explore Software Engineering Keep reading Related customer stories Software engineering Driving digital excellence for a North American automotive leader Read story Software engineering Powering AI-driven travel experiences with DevOps and AWS Read story Software engineering Modernization of healthcare applications in the US Read story More from Adroitent: all customer stories · insights & blog · adroitent.ai

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