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May 2026

Scalable engagement model empowered a global consulting firm GCC

Scalable engagement model empowered a global consulting firm

Leave a Comment / Gcc Offerings, GCC Solutions /

Home/ Customer Stories/ GCC Solutions Customer Story · GCC Solutions Talent, unblocked: 30% lower operational cost How Adroitent’s AI-enabled Managed Resourcing model, powered by the AgileSourcing Framework, removed the talent bottleneck for a global consulting firm — transforming speed-to-hire and cutting operational expenses by 30%. Adroitent Case FileConsultingAgileSourcing · AI Talent3 min read The inflection pointWhen talent becomes the bottleneck to growth The customer is a leading global consulting firm at the forefront of digital innovation, delivering solutions across digital operations, application modernization, data platforms and multi-cloud environments. With ambitious global expansion plans, talent agility was mission-critical to sustaining growth momentum. Inability to onboard right-fit talent at the speed of business Frequent project delays due to resource gaps Growing pressure to support multi-geo expansion with specialized skills Lack of a scalable and responsive talent supply model They didn’t need recruitment support. They needed a strategic talent partner. The interventionAgileSourcing: an AI-enabled talent ecosystem Adroitent stepped in as a transformation partner through its Managed Resourcing model, powered by the AgileSourcing Framework — a holistic, AI-enabled talent ecosystem designed for speed, precision and scalability. AI-driven talent precision Leveraged AATMa and Talentalign to intelligently match candidates to roles Faster, bias-free and highly accurate hiring decisions Always-ready talent cloud A curated, on-demand talent pool across technologies and geographies Zero lag between demand and fulfilment End-to-end lifecycle ownership From sourcing and onboarding to payroll and performance tracking A frictionless, fully managed experience Enterprise-grade governance Backed by CMMI Level 3, ISO 9001 and ISO 27001 aligned processes Quality, security and compliance assured at scale The game changer Real-time visibility and collaboration Integrated dashboards in Power BI and Tableau for live tracking and insights Seamless collaboration via Microsoft Teams, Zoom and Slack AI-based talent management through AATMa and Talentalign Dynamic ramp-up and ramp-down as market conditions shift AATMaTalentalignPower BITableauCMMI L3 By the numbers Speed to hire transformed Operational agility at scale 30% cost optimization Accelerated expansion By the numbers Speed to hire transformed Operational agility at scale 30% cost optimization Accelerated expansion The payoffFaster, leaner, expanding Speed to hire transformed Critical roles were filled faster, eliminating project delays and accelerating delivery timelines. Global talent, on-demand Unlocked access to a diverse, multi-skilled talent pool, enabling seamless global operations. Operational agility at scale Dynamic ramp-up and ramp-down capability allowed the business to adapt instantly to market shifts. 30% cost optimization Streamlined processes and AI-driven efficiencies led to significant cost savings and improved margins. Accelerated business expansion With talent no longer a constraint, the customer confidently expanded into new markets and opportunities. The stack behind itTools & technology AATMaTalentalignNaukri / LinkedIn SourcingMicrosoft TeamsZoomSlackPower BITableau This engagement was delivered as part of Adroitent’s Global Capability Center (GCC) Solutions practice. Good to knowFrequently asked questions What is a scalable engagement model? A scalable engagement model lets an organization expand or contract delivery capacity as demand changes, without renegotiating the relationship each time. It combines a ready talent pool, defined governance and flexible commercial terms so scale is a dial, not a project. How does AI improve talent acquisition? AI can match candidate profiles to role requirements at speed and scale, surface non-obvious matches, and reduce human bias in shortlisting. The result is a shorter time-to-fill and more accurate placements, with recruiters focused on judgement rather than screening. What is time-to-fill and why does it matter? Time-to-fill measures how long it takes to hire for an open role. Long times leave projects understaffed, delay delivery and cost revenue — so it is one of the clearest indicators of whether a talent supply model is actually working. How does managed resourcing reduce operational costs? By consolidating sourcing, onboarding, payroll and performance management with one accountable partner, and by scaling capacity to actual demand. That removes duplicated overhead, avoids paying for idle bench, and reduces delays that cost money downstream. Why do CMMI and ISO certifications matter in outsourcing? They indicate that a partner’s delivery, quality and security practices have been independently assessed against recognized standards. For regulated or enterprise buyers, this provides assurance of process maturity, data protection and repeatable quality at scale. Stop letting talent cap your growth. AI-enabled managed resourcing that scales with your business. Start a conversation Explore GCC Solutions Keep reading Related customer stories GCC solutions Shared services engagement model for GCCs Read story 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 More from Adroitent: all customer stories · insights & blog · adroitent.ai

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A car dealer shaking hands with a couple in front of a vehicle in a showroom.

Driving Digital Excellence for a North American Automotive Leader

Leave a Comment / IT Support, Software Engineering /

Always-On Automotive Platform & GA4 Analytics | Adroitent Home/ Customer Stories/ Software Engineering Customer Story · Automotive · Software Engineering Driving Digital Excellence for a North American Automotive Leader with Always-On Reliability & Data Intelligence A leading North American automotive technology innovator, the customer empowers car dealers with an integrated digital platform that enhances the entire buyer journey — from trade-in valuations and payment calculations to service management and digital retail experiences. The customer’s ecosystem connects dealers and consumers through intelligent, high-performance applications. Adroitent Case FileAutomotiveGA4 · Support & Maintenance3 min read The inflection pointA pivotal inflection point As platform adoption surged, the customer faced a pivotal inflection point: Increasing need for always-on platform stability across dealer networks Rising expectations for faster issue resolution and zero disruption Demand for continuous product evolution to stay competitive Limited visibility into user behavior and engagement insights Urgent need to implement Google Analytics 4 (GA4) for next-gen analytics The challenge was clear: move from reactive support to a predictive, insight-driven digital operations model. The interventionProactive engineering meets data-driven intelligence Adroitent partnered with the customer to deliver a next-generation support and maintenance ecosystem, combining proactive engineering practices with advanced analytics through GA4. Always-On, proactive support model Shifted from reactive fixes to predictive issue resolution Continuous monitoring ensured risks were identified and resolved before impacting dealers or users Continuous innovation engine Delivered incremental enhancements and feature upgrades aligned to evolving business needs Ensured the platform stayed future-ready and competitive Quality built into every release Rigorous unit and functional testing frameworks Guaranteed zero regression and consistent product performance Agile at scale Adopted a Kanban-driven Scrum model Enabled continuous delivery, real-time visibility, and predictable releases The game changer GA4-Powered Data Intelligence Custom GA4 dashboards for real-time traffic and user behavior insights Seamless cross-platform integration ensuring unified analytics Deep visibility into user journeys, engagement patterns, and drop-offs Empowered teams to make faster, and smarter data-backed decisions GA4Custom dashboardsCross-platformReal-time insights By the numbers Improved platform stability Accelerated issue resolution Higher engagement & conversions Data-driven decisions By the numbers Improved platform stability Accelerated issue resolution Higher engagement & conversions Data-driven decisions The payoffFrom stability to strategic advantage Significantly improved platform stability Proactive monitoring and testing led to a sharp reduction in production defects, ensuring seamless dealer and consumer experiences. Accelerated issue resolution Structured support model enabled faster turnaround times, minimizing downtime and improving responsiveness. Higher engagement & conversions GA4-driven insights optimized user journeys, resulting in increased interaction across dealer tools and platforms. True data-driven decision making Teams gained clear, actionable visibility into user behavior, enabling continuous optimization and smarter business strategies. Stronger business outcomes Improved performance, enhanced engagement, and optimized operations, collectively contributed to a measurable improvement in the customer’s bottom line. The stack behind itTools & technology JIRAConfluenceGitHubDockerKafkaPHPLaravelRuby on RailsMongoDBPostgreSQLHerokuPower BI / Tableau This engagement was delivered as part of Adroitent’s Software Engineering Services practice. Good to knowFrequently asked questions What is proactive application support, and how does it differ from reactive support? Proactive support continuously monitors systems to detect and resolve issues before they affect users, using telemetry, alerting and preventive maintenance. Reactive support only responds after something breaks. Proactive models improve uptime, reduce disruption and lower the long-term cost of incidents. What is Google Analytics 4 (GA4) and why did businesses migrate to it? GA4 is Google’s analytics platform built on an event-based data model that tracks users across web and app. Google retired the older Universal Analytics in 2023, so businesses moved to GA4 to keep measuring traffic, engagement and conversions with privacy-focused, cross-platform reporting. How does data analytics improve digital product decisions? Analytics shows how users actually behave — which journeys convert, where they drop off, and which features drive engagement. Teams use these insights to prioritize the roadmap, remove friction, personalize experiences and measure impact, replacing guesswork with evidence. What does ‘always-on’ reliability mean for a digital platform? Always-on reliability means a platform stays available and performant continuously, even during updates or traffic spikes. It is achieved through proactive monitoring, redundancy, automated testing and rapid incident response, so users see minimal downtime and consistent quality. Make your platform always-on. Turn reliability into intelligence — and intelligence into outcomes. Start a conversation Explore Software Engineering Keep reading Related customer stories Software engineering Powering AI-driven travel experiences with DevOps and AWS Read story Software engineering Modernization of healthcare applications in the US Read story Software engineering End-to-end support for a North American AutoTech leader Read story More from Adroitent: all customer stories · insights & blog · adroitent.ai

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Powering AI-Driven Travel Experiences

Powering AI-Driven Travel Experiences with Robust DevOps and AWS Infrastructure Provisioning and Support

Leave a Comment / DevOps, Software Engineering /

Travel AI Platform: DevOps & AWS Automation | Adroitent Home/ Customer Stories/ Software Engineering Customer Story · Travel Tech · Software Engineering Powering AI-Driven Experiences for a Travel Agency with Robust DevOps and AWS Infrastructure Provisioning and Support The customer is a leading travel management company and an industry-innovator in integrating artificial intelligence into the traveler’s journey. As a forward-thinking organization within the travel tech space, they sought to revolutionize how users book trips through an advanced AI-powered Email Bot. Adroitent Case FileTravel TechAWS · DevOps · CI/CD3 min read The inflection pointFrom development to production The customer developed a sophisticated application using Large Language Models (LLMs). The bot allows users to simply email a request (e.g., “Fly from Hyderabad to New York next Monday”), and the system automatically fetches airport lists, selects optimal routes, books tickets, secures hotel rooms, and arranges ground transportation. However, while moving from development to production, the customer faced significant challenges with: Infrastructure management: Unsustainable management of complex backend APIs and LLM models on AWS manually Deployment bottlenecks: Lack of a formalized CI/CD pipeline slowed down the release of new AI features Environment consistency: Faced challenges with code behavior across the environments of development, testing, and production Architectural diversity: Inefficient management of hybrid environment ranging from serverless AWS Lambda, containerized ECS, and traditional EC2 instances Automate the entire lifecycle — for high availability and rapid scalability. The interventionEnd-to-End DevOps & Cloud Infrastructure Modernization Adroitent partnered with the customer to architect and implement a mission-critical DevOps and AWS Infrastructure provisioning — focused on automating the lifecycle of the AI application to ensure high availability and rapid scalability. Automated CI/CD Pipelines After code merge into the target branch, the pipeline automatically triggers the build and deployment process Enabled smooth and consistent deployments across Development, Testing and Production Infrastructure as Code (IaC) with AWS CDK AWS CDK and CloudFormation codified the entire infrastructure, allowing one-click deployment of complex environments All AWS resources — from S3 buckets to networking — version-controlled for consistency, traceability and repeatability Optimized Hybrid Compute Architecture ECS & EC2 for heavy-duty LLM processing; Lambda for serverless, event-driven booking tasks Step Functions orchestrate the Flight → Hotel → Cab logic; SageMaker trains and manages the LLM models The game changer Optimized hybrid compute on AWS AWS ECS & EC2 for heavy-duty LLM processing and persistent backend services AWS Lambda to handle serverless, event-driven tasks within the booking flow AWS Step Functions to orchestrate the complex multi-step booking logic (Flight → Hotel → Cab) AWS SageMaker for training and managing the backend LLM models ECSEC2LambdaStep FunctionsSageMakerS3CloudWatch By the numbers Faster time-to-market Improved reliability Stronger governance Scalability & agility By the numbers Faster time-to-market Improved reliability Stronger governance Scalability & agility The payoffFaster, reliable, scalable delivery Faster Time-to-Market Deployment cycles were reduced from hours/days to minutes, enabling quicker feature releases and faster response to business needs. Improved deployment reliability Automated, consistent deployments minimized manual errors, resulting in more stable releases and fewer production issues. Enhanced operational efficiency Automation reduced manual effort, allowing teams to focus more on innovation and core development activities. Cost optimization Lower operational overhead and reduced rework led to optimized infrastructure and support costs. Stronger governance & compliance Version-controlled infrastructure ensured full traceability, auditability, and adherence to compliance standards. Scalability & agility On-demand environment provisioning enabled rapid scaling and greater flexibility to support evolving business demands. Improved developer experience Simplified, one-click deployments enhanced developer productivity and accelerated onboarding. The stack behind itTools & technology AWS EC2AWS ECSAWS LambdaAWS S3CloudWatchSageMakerStep FunctionsAWS CDKCloudFormationBitbucket PipelinesLLM ModelsPython / Node.js APIs This engagement was delivered as part of Adroitent’s Software Engineering Services practice. Good to knowFrequently asked questions What is Infrastructure as Code (IaC)? Infrastructure as Code manages and provisions cloud resources through version-controlled definition files instead of manual setup. Tools such as AWS CDK, CloudFormation and Terraform let teams review, reproduce and one-click-deploy entire environments, improving consistency, traceability and speed. How does a CI/CD pipeline speed up software delivery? A CI/CD pipeline automatically builds, tests and deploys code whenever changes are merged. Removing manual steps shortens release cycles from days to minutes, reduces human error, and produces consistent, reliable deployments across development, testing and production. When should you use serverless (AWS Lambda) versus containers (ECS/EC2)? Serverless functions like AWS Lambda suit short, event-driven tasks with variable load and no servers to manage. Containers on ECS or EC2 suit long-running, resource-intensive or stateful workloads — such as heavy LLM inference — where you need persistent compute and control. How do you deploy and scale an LLM application on AWS? A common pattern combines SageMaker to train and host models, GPU-backed containers (ECS/EC2) for inference, Lambda for lightweight tasks, and Step Functions to orchestrate multi-step workflows — with autoscaling and Infrastructure as Code to handle demand and keep environments consistent. What are the main benefits of DevOps automation? DevOps automation streamlines building, testing, deploying and monitoring software. It shortens release cycles, reduces manual errors, improves reliability and governance, and frees engineers to focus on new features rather than repetitive operational work. Make your platform deploy-in-minutes. Automate your cloud lifecycle — from code to production, at scale. 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 Modernization of healthcare applications in the US Read story Software engineering End-to-end support for a North American AutoTech leader Read story More from Adroitent: all customer stories · insights & blog · adroitent.ai

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Enterprise AI Services

Enterprise AI Services Empowered a Global Publishing Leader with an AI Agent

Leave a Comment / AI, DROIT /

Enterprise AI Agent for Publishing: 4X Growth | Adroitent Home/ Customer Stories/ Artificial Intelligence Customer Story · Publishing · Artificial Intelligence Enterprise AI services helped develop an AI Agent that improved the customer’s reader user base by 4X and their ROI by 3X. The customer is a global leading publishing company offering a range of professional print and digital publications, books, and textbooks in specialties such as health, tax, accounting, and legal. The group serves customers in over 100 countries catering to millions of users across the globe. Adroitent Case FilePublishingAzure OpenAI · LLM3 min read The inflection pointCustomer Business Need In today’s age with more importance to customer experience dominating all businesses, this publishing services customer also wanted to ease the access of its journals and articles to millions of readers and were looking around the possibilities of an AI solution. Ease and quicken the display from their existing collection of journals and articles — which runs in millions — to users based on every user search An AI agent to seamlessly access articles and journals by delivering topic-page generation, article summaries and results summaries Other much-needed publishing-industry in-demand use cases to enhance user experience Adroitent’s Enterprise AI services improved the customer’s reader user base by 4X and their ROI by 3X. The interventionSolution Delivered — an AI agent leveraging LLMs Adroitent partnered with the customer to develop an AI agent (engine) leveraging LLMs to quicken user access — with a topic-page generator, article summary generator, search results summary generator, and various other use cases to engage their readers. Topic-page AI Generator Automatically creates topic-focused pages, with related articles, related topics and topical ads for monetization Below the topic page, the article summary and source-articles list are also displayed Related Questions AI Generator For any topic or keyword searched, generates all related questions to quicken access to the required content instantly Addresses long-tail keywords and niche queries to attract a wider audience and drive organic traffic Article Summary AI Generator Instantly generates an article summary in a few lines for any article, lengthy or small Identifies key points and provides a shorter version while retaining its essential meaning Article Results Summary AI Generator For any keyword searched, generates all keyword-related results summaries Enables users to quickly pick the accurate article of their choice, with error-free extraction Solution highlights The Topic-page Generator Deep-dived into the customer’s database to discover the topic name and search the matched terms The AI agent picked the most relevant or trending subheadings for the topic page The top 10 subheadings were arranged in a sequence for a given or chosen topic keyword Article abstracts were used to create summaries, with quick validation and authentication of results Azure GPT-4.0PythonSQL ServerAWSLLMs By the numbers 4X reader user base 3X ROI Higher organic traffic Better reader engagement By the numbers 4X reader user base 3X ROI Higher organic traffic Better reader engagement The payoff4X readers, 3X ROI Faster Access to Information The AI agent generates concise summaries of complex topics, helping users quickly find and understand the information they need. Increases Organic Traffic By targeting long-tail keywords and niche queries, it attracts a broader audience and boosts organic search visibility. Improves Reader Engagement The AI agent enhances the overall user experience by delivering accessible, relevant, and personalized content. Delivers Actionable Insights It enables quick analysis of audience data to identify trending topics, helping publishers tailor content to user interests. Drives Content Ideation The topic-page generator suggests related and emerging themes, supporting the creation of fresh and relevant content. Enhances Learning Outcomes By offering structured and engaging content delivery, the AI agent enriches the learning experience for readers. Supports Data-Driven Strategy Performance tracking of AI-generated content provides insights into reader behavior, informing future content planning. Accelerates Topic Discovery The AI suggests related articles and topics based on user behavior, broadening content exposure and user exploration. Saves Time Summarizes long-form content into digestible snippets, enabling users to absorb key insights quickly. The stack behind itTools & technology Azure GPT-4.0PythonSQL ServerAWSLLMs This engagement was delivered as part of Adroitent’s Artificial Intelligence Services practice. Good to knowFrequently asked questions What is an AI agent? An AI agent is a software system that uses large language models and tools to autonomously carry out multi-step tasks — retrieving information, reasoning over it and generating outputs like summaries or pages — with little human intervention, adapting its actions to each request. How do large language models summarize articles? An LLM reads the full text, identifies the main ideas and relationships, and generates a shorter version in natural language. Extractive and abstractive techniques, prompting and retrieval help produce accurate, concise summaries while preserving meaning. What is a topic-page generator in digital publishing? A topic-page generator automatically assembles a focused page for a subject — pulling relevant articles, related topics, summaries and sometimes ads — using AI to select and sequence the most relevant content, helping publishers scale content and improve discovery. How does AI improve content discovery and SEO for publishers? AI surfaces relevant content by matching user intent, generating related questions and long-tail topic pages, and summarizing results. This improves internal discovery, targets niche queries, and increases organic search visibility and reader engagement. Which models power enterprise AI content applications? Enterprise content applications commonly pair hosted LLMs such as Azure OpenAI’s GPT-4 family with databases like SQL Server and cloud infrastructure such as AWS to retrieve, generate and serve content securely at scale. Put an AI agent on your content. Turn your library into instant, personalized reader experiences. Start a conversation Explore Artificial Intelligence Keep reading Related customer stories GCC solutions Scalable engagement model empowered a global consulting firm Read story 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 More from Adroitent: all customer stories · insights & blog · adroitent.ai

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