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Scalable engagement model empowered a global consulting firm GCC

Scalable engagement model empowered a global consulting firm

Leave a Comment / Gcc Offerings, GCC Solutions /

Scalable engagement model empowered a global consulting firm with streamlined operations and a 30% reduction in operational expenses About the Customer A leading global consulting firm at the forefront of digital innovation, the customer delivers cutting-edge solutions across digital operations, application modernization, data platforms, and multi-cloud environments. With ambitious global expansion plans, talent agility was mission-critical to sustaining their growth momentum.  Business Challenge When talent becomes the bottleneck to growth Despite strong market demand, the customer faced a critical constraint:  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 What they needed wasn’t just recruitment support but it was a strategic talent partner who could operate at the speed, scale, and complexity of their business.  The Solution in detail 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.  What Made the Difference?  AI-driventalent precision Leveraged AATMa and Talentalign to intelligently match candidates to roles   Enabled faster, bias-free, and highly accurate hiring decisions   Always-ready talent cloud Built a curated, on-demand talent pool across technologies and geographies   Ensured zero lag between demand and fulfilment   End-to-Endtalent lifecycle ownership From sourcing and onboarding to payroll and performance tracking   Delivered a frictionless, fully managed experience   Enterprise-grade governance Backed by CMMI Level 3, ISO 9001, ISO 27001 aligned processes   Ensured quality, security, and compliance at scale   Real-Timevisibility & collaboration Integrated dashboards (Power BI, Tableau) for live tracking and insights   Seamless collaboration via Teams, Zoom, and Slack   Business ROI Speed to hire transformed Operational agility at scale 30% cost optimization Accelerated business expansion Tools & Technology Leveraged Sourcing Portals: Leading Sourcing portals such as Naukri, Linkedin, and others were used to source candidates.  AI-based Talent Management Platform: AATMa (Adroitent Advanced Talent Management Platform) and Talentalign (Native AI and Agentic AI Recruitment and ATS Platform)  Communication: Microsoft Teams, Zoom, Slack  Dashboards & Reporting: Power BI, Tableau  Business Outcomes 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. Talk To Our Experts

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Driving Digital Excellence for a North American Automotive Leader with Always-On Reliability & Data Intelligence

Driving Digital Excellence for a North American Automotive Leader

Leave a Comment / IT Support, Software Engineering /

Home/Customer Stories/Software Engineering Customer Story · Software Engineering Always-on, by design: turning reliability into data intelligence How a North American automotive technology leader moved from reactive fixes to a predictive, insight-driven digital operations model — with Adroitent. Adroitent Case File Automotive Platform GA4 · Always-On Support 4 min read Scroll ALWAYS-ON ✦ PROACTIVE SUPPORT ✦ GA4 REALTIME ✦ ZERO REGRESSION ✦ CONTINUOUS DELIVERY ✦ DATA-DRIVEN ✦ ALWAYS-ON ✦ PROACTIVE SUPPORT ✦ GA4 REALTIME ✦ ZERO REGRESSION ✦ CONTINUOUS DELIVERY ✦ DATA-DRIVEN ✦ 01 The inflection point Success started to strain the system As platform adoption surged across dealer networks, the customer hit a pivotal moment — a support model that could only react after something broke was running out of road. 01Always-on platform stability across dealer networks 02Faster issue resolution with zero disruption 03Continuous product evolution to stay competitive 04Visibility into user behavior and engagement 05Urgent move to Google Analytics 4 (GA4) “ Go from reactive to predictive, insight-driven operations. Stabilize → then unlock the data. Stabilize → then unlock the data. Stabilize → then unlock the data. Stabilize → then unlock the data. 02 The intervention Proactive engineering, meet data intelligence Adroitent built a next-gen support and maintenance ecosystem — pairing proactive engineering with advanced analytics through GA4. (hover a card) 1 Always-on, proactive support Shifted from reactive fixes to predictive issue resolution Continuous monitoring caught risks before they reached dealers or users Hover to expand 2 Continuous innovation engine Incremental enhancements and feature upgrades aligned to business needs Kept the platform future-ready and competitive Hover to expand 3 Quality in every release Rigorous unit and functional testing frameworks Guaranteed zero regression and consistent performance Hover to expand 4 Agile at scale Kanban-driven Scrum model Continuous delivery, real-time visibility, predictable releases Hover to expand The game changer GA4-powered data intelligence Custom GA4 dashboards for real-time traffic and user-behavior insights Seamless cross-platform integration for unified analytics Deep visibility into user journeys, engagement, and drop-offs Teams empowered to make faster, smarter, data-backed decisions 418realtimeusers▲ live last 30 minupdating… 03 The payoff Reliability compounded into results Rock-solid platform stability Engagement & conversions up Decisions driven by data Stronger business outcomes In detail — tap to open Significantly improved platform stabilityProactive monitoring and testing led to a sharp reduction in production defects, ensuring seamless dealer and consumer experiences. Accelerated issue resolutionA structured support model enabled faster turnaround times, minimizing downtime and improving responsiveness. Higher engagement & conversionsGA4-driven insights optimized user journeys, increasing interaction across dealer tools and platforms. True data-driven decision makingTeams gained clear, actionable visibility into user behavior, enabling continuous optimization and smarter strategies. Stronger business outcomesImproved performance, enhanced engagement, and optimized operations collectively contributed to a measurable improvement in the customer’s bottom line. The stack behind it Tools & technology JIRAConfluence GitHubDocker KafkaPHP LaravelRuby on Rails MongoDBPostgreSQL HerokuPower BI / Tableau FAQ Frequently 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 it always-on. Turn reliability into intelligence — and intelligence into outcomes. Talk to our experts Keep reading Related customer stories Software engineering AI-driven travel experiences, powered by DevOps & AWS Read story Software engineering Modernizing healthcare applications in the US Software engineering End-to-end support for a North American AutoTech leader Read story View all customer stories  ·  Explore Software Engineering services

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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 /

Home/Customer Stories/Software Engineering Customer Story · Cloud & DevOps Travel, automated: DevOps + AWS for an AI booking bot How Adroitent gave a travel-tech innovator a fully automated DevOps and AWS foundation — turning an LLM-powered email booking bot from manual deployments into a scalable, one-click cloud platform. Adroitent Case FileTravel TechAWS · DevOps · CI/CD3 min read Scroll to read AUTOMATED CI/CD ✦ INFRASTRUCTURE AS CODE ✦ AWS CDK ✦ ONE-CLICK DEPLOYS ✦ SERVERLESS + GPU ✦ SCALABLE ✦ AUTOMATED CI/CD ✦ INFRASTRUCTURE AS CODE ✦ AWS CDK ✦ ONE-CLICK DEPLOYS ✦ SERVERLESS + GPU ✦ SCALABLE ✦ 01The inflection point Success outgrew manual infrastructure The customer built a sophisticated LLM-powered email booking bot — email a request like “Fly from Hyderabad to New York next Monday” and it books flights, hotels and ground transport. Moving from development to production exposed hard infrastructure limits. 01Infrastructure management: managing complex backend APIs and LLM models on AWS manually was unsustainable 02Deployment bottlenecks: no formalized CI/CD pipeline slowed the release of new AI features 03Environment consistency: code behaved differently across development, testing and production 04Architectural diversity: inefficient management of a hybrid estate spanning AWS Lambda, ECS and EC2 “ Automate the entire lifecycle — for high availability and rapid scale. From manual deploys → to one-click cloud. From manual deploys → to one-click cloud. From manual deploys → to one-click cloud. From manual deploys → to one-click cloud. 02The intervention End-to-end DevOps & cloud modernization Adroitent architected and implemented a mission-critical DevOps and AWS infrastructure, automating the AI application’s lifecycle for high availability and rapid scalability. (hover a card) 1 Automated CI/CD pipelines Fully automated Bitbucket Pipeline, triggered on merge to the target branch Smooth, consistent deployments across Development, Testing and Production Hover to expand 2 Infrastructure as Code AWS CDK + CloudFormation codified the entire infrastructure for one-click deployment Every resource — S3 to networking — version-controlled for consistency and traceability Hover to expand 3 Optimized hybrid compute ECS & EC2 for heavy LLM processing; Lambda for event-driven booking tasks Step Functions orchestrate Flight → Hotel → Cab; SageMaker trains and manages the models Hover to expand The game changer Serverless-to-GPU hybrid compute on AWS AWS ECS & EC2 for heavy-duty LLM processing and persistent backend services AWS Lambda for serverless, event-driven tasks within the booking flow AWS Step Functions to orchestrate multi-step booking logic (Flight → Hotel → Cab) AWS SageMaker to train and manage the backend LLM models AWS architecturelive ECSEC2LambdaStep FunctionsSageMakerS3CloudWatch 03The payoff Ship in minutes, scale on demand Faster time-to-market Enhanced operational efficiency Stronger governance & compliance Scalability & agility In detail — tap to open Faster time-to-marketDeployment cycles dropped from hours or days to minutes, enabling quicker feature releases and faster response to business needs. Improved deployment reliabilityAutomated, consistent deployments minimized manual errors, resulting in more stable releases and fewer production issues. Enhanced operational efficiencyAutomation reduced manual effort, freeing teams to focus on innovation and core development. Cost optimizationLower operational overhead and reduced rework optimized infrastructure and support costs. Stronger governance & complianceVersion-controlled infrastructure ensured full traceability, auditability and adherence to compliance standards. Scalability & agilityOn-demand environment provisioning enabled rapid scaling and greater flexibility for evolving demands. Improved developer experienceSimplified, one-click deployments boosted developer productivity and accelerated onboarding. The stack behind it Tools & technology AWS EC2 AWS ECS AWS Lambda AWS S3 CloudWatch SageMaker Step Functions AWS CDK CloudFormation Bitbucket Pipelines LLM Models Python / Node.js APIs FAQ Frequently asked questions What is Infrastructure as Code (IaC)?Infrastructure as Code manages and provisions computing resources through machine-readable definition files instead of manual setup. Tools like AWS CDK, CloudFormation and Terraform let teams version, review and reproduce entire environments consistently, cutting errors and enabling one-click, repeatable deployments. How does a CI/CD pipeline speed up software delivery?A CI/CD pipeline automatically builds, tests and deploys code whenever changes are merged. By removing manual steps it shortens release cycles from days to minutes, catches problems earlier, and delivers more frequent, more reliable releases with less risk. 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 fit long-running, resource-intensive or stateful workloads — such as heavy AI/LLM processing — where you need more control over compute and memory. How do you deploy and scale an LLM application on AWS?A common pattern uses SageMaker to train and host models, containers (ECS/EC2 or GPU instances) for inference-heavy work, Lambda for lightweight tasks, and Step Functions to orchestrate multi-step logic — all provisioned via Infrastructure as Code so the stack scales on demand. What are the main benefits of DevOps automation?DevOps automation delivers faster time-to-market, more reliable and repeatable deployments, lower operational cost, stronger governance through version-controlled infrastructure, and easier scalability — while freeing engineers to build features instead of managing environments. Make your platform deploy-in-minutes. Automate your cloud lifecycle — from code to production, at scale. Talk to our experts 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 View all customer stories  ·  Explore Software Engineering services  ·  Read the blog

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Enterprise AI Services

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

Leave a Comment / AI, DROIT /

Home/Customer Stories/Artificial Intelligence Customer Story · Artificial Intelligence The publishing AI agent: 4× readers, 3× ROI How Adroitent built an LLM-powered AI agent for a global publisher — generating topic pages, article summaries and search results on demand across a library of millions of journals and articles. Adroitent Case FilePublishingLLM · Azure OpenAI3 min read Scroll to read TOPIC-PAGE AI ✦ ARTICLE SUMMARIES ✦ SEARCH SUMMARIES ✦ 4× READERS ✦ 3× ROI ✦ LLM-POWERED ✦ TOPIC-PAGE AI ✦ ARTICLE SUMMARIES ✦ SEARCH SUMMARIES ✦ 4× READERS ✦ 3× ROI ✦ LLM-POWERED ✦ 01The inflection point Millions of articles, hard to surface The customer — a global publisher of health, tax, accounting and legal titles serving readers in over 100 countries — wanted to make its vast library of journals and articles instantly accessible to millions of users. 01Instant discovery at scale: surface the right content from a collection running into millions of journals and articles on every user search 02AI-native reader experiences: deliver topic-page generation, article summaries and results summaries seamlessly 03Publishing-specific use cases: meet in-demand industry needs that enhance user experience and engagement “ Turn a library of millions into instant, personalized answers. Millions of articles → instant, personalized answers. Millions of articles → instant, personalized answers. Millions of articles → instant, personalized answers. Millions of articles → instant, personalized answers. 02The intervention An LLM-powered AI agent for publishing Adroitent developed a scalable AI agent (engine) leveraging LLMs — a topic-page generator, article-summary generator and search-results summary generator — plus other publishing use cases to engage readers. (hover a card) 1 Topic-page AI generator Auto-creates topic-focused pages with related articles, related topics and topical ads for monetization Displays article summaries and the source-article list below the topic page Hover to expand 2 Related-questions AI generator For any searched topic, generates related questions that match real user intent Targets long-tail queries to boost organic traffic and engagement Hover to expand 3 Article-summary AI generator Instantly condenses any article — long or short — into a few clear lines Helps busy professionals, students and researchers grasp key points fast Hover to expand 4 Search-results summary AI generator Summarizes all results for a keyword so users pick the right article quickly Error-free extraction, scalable for large content operations Hover to expand The game changer Topic-page generation, powered by LLMs Deep-dived the customer’s database to discover the topic and match search terms Fetched and ranked the most relevant / trending subheadings for each topic page Sequenced the top 10 subheadings for any chosen topic keyword Generated article-abstract summaries and validated results for accurate display AI agentlive Azure GPT-4.0PythonSQL ServerAWSLLMs 03The payoff 4× readers, 3× ROI Faster access to information 4× larger reader user base 100% more accurate article display 3× improved ROI In detail — tap to open Faster access to informationThe AI agent generates concise summaries of complex topics, helping users quickly find and understand the information they need. Increased organic trafficBy targeting long-tail keywords and niche queries, it attracts a broader audience and boosts organic search visibility. Improved reader engagementThe AI agent enhances the overall user experience by delivering accessible, relevant and personalized content. Actionable insightsQuick analysis of audience data identifies trending topics, helping publishers tailor content to reader interests. Faster content ideationThe topic-page generator suggests related and emerging themes, supporting fresh, relevant content creation. Enhanced learning outcomesStructured, engaging content delivery enriches the learning experience for readers. Data-driven strategyPerformance tracking of AI-generated content provides insight into reader behaviour and informs future content planning. The stack behind it Tools & technology Azure GPT-4.0 Python SQL Server AWS LLMs FAQ Frequently 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 vector search, SQL or NoSQL databases and cloud infrastructure like AWS or Azure to retrieve, generate and serve content securely at scale. Put an AI agent on your content. Turn your library into instant, personalized reader experiences. Talk to our experts 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 View all customer stories  ·  Explore Artificial Intelligence services  ·  Read the blog

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Empowering Modernization of Healthcare Applications for a Leading Global Healthcare Technology Solutions Provider in the US

Modernization of Healthcare Applications for Cerner Healthcare Technology Provider

.Net, Software Engineering /

Modernization of Healthcare Applications for Cerner Healthcare Technology Provider About the Customer Our Customer is a global leader in health information technology, dedicated to advancing healthcare delivery and improving the health of communities worldwide. The organization serves over 27,000 facilities globally, including more than 2,650 hospitals, 3,750 physician practices, 40 employer sites, and 1,600 retail pharmacies. By delivering integrated clinical and financial platforms, Cerner empowers healthcare providers streamline operations and manage revenue cycles efficiently through innovative technology. Challenge As a massive global entity, Cerner faced the complex task of evolving its legacy systems to meet modern healthcare standards. Key challenges included: Legacy systems transformation: A large portfolio of legacy Visual Basic applications required migration to a more scalable, efficient, and future-ready platform. Interoperability challenges: There were significant gaps in seamless data exchange across clinical, financial, and enterprise systems, necessitating the adoption of advanced integration capabilities. Application modernization needs: Critical applications, including inpatient pharmacy and registration modules within the Customer’s Millennium Suite, required a structured modernization approach to enhance functionality and performance. Reliability and performance assurance: Mission-critical production applications needed strengthened operational oversight to ensure high availability, stability, and optimal performance. Solution Adroitent played a pivotal role in the architecture, design, and integration of core solutions within the Cerner ecosystem. Key contributions included: Design, and development Modernization approach Interoperability & API development  Quality engineering  Crash analysis and performance tuning Agile methodology Adroitent maintained a long-standing, high-capacity partnership with the customer to ensure the continued stability and seamless operation of their mission-critical systems. Duration of involvement: 10+ YearsTeam size: 45+ specialized professionalsMethodology: Agile    The Solution in detail Design, and development: Led architecture, design, development, quality certification, integration, and support for multiple core solutions within the Customer’s Healthcare ecosystem. Modernization approach: Defined target architectures for mission-critical uplift programs and successfully migrated legacy Visual Basic applications to the .NET platform that improved the scalability, performance, and maintainability. Teams also played a key role in defining the target architecture for multiple mission-critical uplift programs such as Inpatient Pharmacy and Registration from their Healthcare Millennium Suite. Interoperability & API development: Designed and implemented healthcare integrations using HL7 and FHIR standards. This included building FHIR-based RESTful APIs to support both modern and legacy hybrid integration scenarios. Quality engineering: Developed custom quality certification and testing tools to strengthen automated quality control. Ensured system stabilization by conducting rigorous performance and memory testing and resolved root-cause issues through crash analysis to ensure system stability. Crash analysis and performance tuning: Stabilized mission-critical applications through in-depth crash analysis, performance tuning, and root-cause resolution to ensure production reliability. Agile methodology: Adopted Agile practices, delivering features through structured sprint cycles to ensure timely implementation, continuous feedback, and iterative improvement.   Business ROI Enhanced scalability Seamless interoperability Improved quality & reliability Clinician-centric results Tools & Technology Leveraged Adroitent leveraged a comprehensive suite of healthcare and enterprise technologies to deliver the engagement effectively. Standards: Secure network protocols, HL7/DICOM standards, FHIR R4, CDA, and X12 (EDI) Frameworks: SMART on FHIR, .NET Platform. Middleware/Engines: Open Engine, Mirth, Rhapsody, and Cloverleaf. Security: OAuth 2.0, TLS/SSL, and Token-based exchange. Business Outcomes Enhanced scalability: Successful migration to .NET improved the performance and maintainability of the customer’s critical healthcare applications. Seamless interoperability: Enabled unified data flow across labs, radiology, pharmacy, and external devices via standardized FHIR and HL7 interfaces. Improved quality & reliability: Automated quality control and root-cause resolution led to stabilized production environments for mission-critical apps. Clinician-centric results: Modernized UIs (PowerChart) and streamlined clinical workflows allowed physicians to focus on patient care rather than the underlying technology. Talk To Our Experts

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Staffing Provider

Databricks-Based Predictive Analytics Improved Demand and Placement Forecasting Accuracy for a Leading US Staffing Provider

Leave a Comment / Databricks /

About the Customer A large healthcare provider network operating across multiple facilities aimed to modernize its data and analytics environment to improve clinical, operational, and financial insights while maintaining strong controls over sensitive healthcare information. Business Challenge ·       Data silos across EHR, claims, laboratory, radiology, and other clinical and operational systems, limiting unified data access and insights. ·       Slow analytics and delayed reporting caused by legacy and fragile ETL pipelines, impacting timely decision-making. ·       Limited data governance and inconsistent access controls, creating challenges in securely managing sensitive PHI datasets.   ·       Challenges in enabling ML and AI use cases such as readmission risk prediction, capacity forecasting, and revenue leakage detection due to unreliable and duplicated data sources. About Customer Our customer is a leading US-based staffing and workforce solutions provider, supporting multiple industries at scale. The customer has a network of over 10,000 contractors across the healthcare, IT, and financial services sectors. Customer Challenge The staffing provider faced several challenges that limited its ability to make proactive, data-driven decisions: Fragmented data across ATS, CRM, VMS, payroll, and other systems Manual forecasting of demand and candidate availability Inability to accurately predict placement success, employee attrition, and revenue trends Limited reporting without predictive or forward-looking insights Growing global services needed seamless insights for informed decision making Slow Time-to-Fill (TTF) rates for niche roles The customer wanted to modernize its analytics platform to improve talent demand forecasting, candidate placement accuracy, and improve overall operational efficiency. With rapidly growing data volumes across recruitment, sales, and delivery systems, the organization needed a scalable, intelligent solution to stay competitive and needed an Analytics implementation partner. Solution Delivered: Predictive Analytics Adroitent leveraged Databricks platform to unify data, apply advanced analytics, and operationalize machine learning models across the staffing lifecycle to empower the customer with streaming and real-time analytics. Key Solution Components Unified Data Platform: Consolidated structured and semi-structured data from ATS, CRM, VMS, and payroll into Databricks using Delta Lake. Medallion Architecture (Bronze, Silver, Gold): Bronze: Raw ingestion of candidate, job, placement, and sales data was done Silver: Cleaned, standardized, and enriched datasets Gold: Curated analytics and ML-ready feature tables Governance & Security: Deployed Unity Catalog to ensure that sensitive PII (Personally Identifiable Information) was secure and compliant with US data privacy regulations. Centralized governance with Unity Catalog, ensuring secure role-based access and audit-ready data controls. ML Model Enablement & Predictive Analytics: Demand forecasting for open roles and future requisitions Candidate placement success prediction Attrition and redeployment risk scoring Revenue and pipeline forecasting BI Analytics within Databricks Delivered predictive insights through dashboards integrated with existing Databricks reporting tools for recruiters, sales leaders, and C-level executives. Project Highlights Adroitent implemented a value-driven delivery methodology comprising: Business use case discovery aligned with staffing KPIs Analytics-optimized Lakehouse architecture design Accelerated data engineering through reusable frameworks and components ML model development and validation using Databricks MLflow Performance tuning and production deployment Ongoing managed services and continuous platform improvement Technology Stack Leveraged Databricks Platform, Delta live tables, MLFlow Business Outcome The predictive analytics solution built on Databricks generated measurable business impact, including: Improved forecasting accuracy for demand and placements Faster decision-making enabled by actionable data insights Streamlined workforce planning with reduced manual intervention Better placement outcomes through predictive demand insights Enhanced operational efficiency organization-wide Stronger global business growth

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Secure and Compliant Healthcare Data Access Enabled through Databricks Lakehouse Implementation

Databricks /

About the Customer A large healthcare provider network operating across multiple facilities aimed to modernize its data and analytics environment to improve clinical, operational, and financial insights while maintaining strong controls over sensitive healthcare information. Business Challenge ·       Data silos across EHR, claims, laboratory, radiology, and other clinical and operational systems, limiting unified data access and insights. ·       Slow analytics and delayed reporting caused by legacy and fragile ETL pipelines, impacting timely decision-making. ·       Limited data governance and inconsistent access controls, creating challenges in securely managing sensitive PHI datasets.   ·       Challenges in enabling ML and AI use cases such as readmission risk prediction, capacity forecasting, and revenue leakage detection due to unreliable and duplicated data sources. Solution Delivered: Databricks Lakehouse Architecture Adroitent implemented Databricks Lakehouse architecture using the Medallion pattern (Bronze, Silver, and Gold) to create a unified, governed, and AI-ready data platform. Key Solution Components Data Ingestion (Bronze): Data ingestion from EHR records, claims data, HL7/FHIR messages, and lab data Enabling raw data into Delta live tables to support data reliability  Data Transformation (Silver): Standardization, deduplication, patient/provider entity resolution, code normalization (ICD/CPT), and data quality rules Pipeline orchestration via Databricks-native workflows and structured monitoring Data Curated (Gold): Curated data for quality measures, revenue cycle dashboards, clinical ops, and population health BI enablement and model-ready feature tables for ML model orchestration Project Highlights Data reliability and performance: Databricks Lakehouse reference patterns helped ingest and transform data. To improve trust and auditability, the platform was standardized on Delta Live tables, leveraging capabilities such as ACID reliability, schema enforcement, and historical versioning. Governance, Security & HIPAA-Aligned Controls: Governance was implemented using Unity Catalog to enable centralized access control, auditing, and lineage across workspaces and data assets. Various Security controls included: Role-based and attribute-based access for PHI vs non-PHI datasets (row/column-level controls where required) Audit logging and lineage visibility for compliance and investigations HIPAA-aligned configuration approach based on Databricks HIPAA guidance (PHI handling, security posture, and operational controls). BI + ML Model enablement: The MLFLow was used on a single platform with curated marts and feature-ready datasets. The ML models were fine-tuned based on the business use case and implemented. Technology Stack Databricks Platform, Delta live tables, MLFlow Business Outcome Faster patient data onboarding by using reusable data ingestion patterns Improved patient reports (Streaming and real-time analytics) Secure, compliant access to sensitive healthcare data thru effective governance Accelerated reporting through comprehensive dashboards, enabling faster and more informed decision-making. Improved trust in analytics through standardized, curated datasets and governed data access. Accelerated the customer’s ML initiatives with feature-ready data products

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Modernizing and Maintaining a Legacy Platform

Modernizing and Maintaining a Legacy Legal Management System

IT Support, Software Engineering /

Modernizing and Maintaining a Legacy Legal Management System About the Customer The customer is a leading legal management software provider, offering enterprise-grade solutions to multiple legal firms. They are a prominent provider of financial and practice management software and has been empowering law firms for over four decades. They develop software for mid-sized law firms that enhances workflow efficiency and maximizes financial performance. Business Challenge The customer’s core application, LMS spans multiple layers and versions, built on legacy platforms like IBM AS/400 with RPG and LANSA for back-end processing, and PowerBuilder and Java for front-end display and reporting. The customer needed ongoing maintenance and modernization support for a complex, multi-layered LMS environment (LMS 4, LMS 5, and LMS Plus). They also faced a major US regulatory requirement to revamp 1099 tax reporting formats (MISC, NEC) from PDF to CSV.   Key challenges included Maintaining LMS 4 (legacy RPG/LANSA system) Supporting feature enhancements across LMS 4, LMS 5, and LMS Plus Integrating compliance-driven changes within short timelines Managing multi-channel communication and ticket lifecycle via Zendesk & Jira Navigating VPN access/security issues while collaborating remotely The Solution in detail Adroitent provided end-to-end ownership of modernizing, support and enhancement activities across all LMS layers of the customer’s system. Feasibility analysis: Initially a full lifecycle delivery with Feasibility analysis was taken up for code change (RPG, LANSA) and new feature addition for LMS Plus. Testing process: Unit testing, System Integration testing, and User Acceptance Testing were done in coordination with the product owner. Bug fixes were successfully done with issue resolutions at every stage. All peripheral applications were tested end-to-end. 1099 Tax Reporting Overhaul: Rebuilt tax form logic, shifted output format from PDF to CSV, ensured compliance with latest U.S. Government Standards. Adopted Agile practices: Daily updates for SCRUM ceremonies involving sprint planning, backlog refinement, and direct customer communication were a part of the project activities. Business ROI Ensured Business Continuity Delivered 1099 compliance  Streamlined ticket lifecycle Resolved VPN connectivity issue Tools & Technology Leveraged Back-end Development: RPG, Control Language (CL), LANSA Front-end Layers: PowerBuilder, Java Ticketing & Collaboration: Zendesk, Jira, Confluence Testing tools: Selenium Business Outcomes Ensured business continuity for LMS platform with proactive maintenance Successfully delivered 1099 compliance project within 2.5 months across LMS 4 & LMS 5 Improved collaboration and visibility using Jira and Confluence workflows Streamlined ticket lifecycle from Zendesk to resolution with full ownership Resolved critical VPN access issues, ensuring secure and seamless remote support Strengthened trust with the customer through consistent delivery and rapid response to production issues enhancing the performance and operational efficiency Talk To Our Experts

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End-to-End Support Services for a North American AutoTech Leader

IT Support, Software Engineering /

Modernizing and Maintaining a Legacy Platform (IBM AS/400) Legal Management System (LMS) for Enhancing Platform’s Operational Efficiency About the Customer Our customer is a tech-forward automotive platform provider serving many dealers and consumers across North America. Their software suite powers trade-in valuations, payment calculators, digital brochures, videos for sales and service, and much more. With over 7,000 dealer websites utilizing its capabilities, the company is leading the auto tech industry with advanced platforms. Customer Challenges The customer’s platform had issues and problems that spanned across configuration, data/feeds, and code, often around product boundaries and hosting environments. Some of the pain points were: Fragmented handoffs between support triage and engineering Configuration issues and code issues Handling issues of core services enabled by the customer to their dealers   Solution Delivered Adroitent was chosen as the preferred partner by the customer to enable end-to-end support for their applications. Adroitent teams deep dived into the customer’s system and enabled an integrated support model that aligned with the customer’s business goals. This ensured single-threaded ownership from incident intake to fix and release for configuration, code issues, etc. Procedure adopted for Issue Resolution Dealers reporting issues: Dealers initially coordinated with the customer’s support team at the first level to report their issue. A preliminary investigation was performed by the customer’s support team for issue resolution by diagnosing and identifying the issue. Issue handling: Minor issues were handled by the support team; unresolved issues were logged in Jira, which were handled by Adroitent’s team. Issue type identification: Issues were categorized into Config issues, code issues, or data issues. A Jira ticket was created and assigned to the developer. The developers resolved the issue which was further tested and finally deployed to the production environment. Technology Leveraged Issue & Project Management: Jira Frontend & SDK: TypeScript/JavaScript, Angular Analytics: GA4, Shift Analytics Source Control: GitHub Deployments: Heroku (multi-environment) Cloud & Hosting: AWS Business Outcomes By partnering with Adroitent, the customer achieved significant improvements in issue resolution efficiency. Our team ensured that complex challenges were addressed swiftly and effectively, minimizing disruptions across their extensive dealer network. This strategic support model enabled the customer to concentrate on product innovation and core business operations while maintaining reliable, uninterrupted services for their dealers. We enabled a seamless dealer experience driven by faster triage, proactive resolution, and enhanced service continuity.

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What is the Role of AI Agents in Recruitment in 2025?

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What is the Role of AI Agents in Recruitment in 2025? Table of Contents Introduction. Major Recruitment Challenges Faced by Recruiters in 2025.  What is the Role of AI Agents in Recruitment?. Transformative Role of AI Agents in Recruitment. What are the Benefits of AI-driven Recruitment for Enterprises. Data Privacy and Compliance Aspects to Ponder. The Future Outlook of AI Agents in Recruitment in 2025. Conclusion. Adroitent’s Talentalign Introduction In 2025, Artificial Intelligence (AI) agents are playing a pivotal role in reshaping recruitment strategies for enterprises and multinationals, making the process faster, data-driven, and candidate-centric. These AI agents are bound to transform every facet of the recruitment life cycle, moving it from a time-consuming process to a proactive, strategic, and efficient process. Major Recruitment Challenges Faced by Recruiters in 2025 Traditional recruiters relied more on manual screening, smaller networks, and older versions of Applicant Tracking Systems (ATS). Some of their common challenges include: Sifting resumes: Recruiters struggle manually sifting through thousands of applications for specific skill sets that becomes a time-consuming task. Greater competition: While drawing from global talent pools, recruiters face intense competition from others for the same pool of skilled professionals. Timely hiring: Businesses need faster hiring for project-specific roles by recruiters. Any delay in hiring impacts project delivery, innovation cycles, and ultimately brand value. Candidate experience: Due to delayed feedback from recruiters, the candidates might shift to other sources. Today’s candidates expect faster feedback and seamless experience from first touchpoint to offer acceptance. Thus, traditional Human Resource Information Systems (HRIS) and Applicant Tracking Systems (ATS) alone are no longer sufficient. This is where AI agents step in as the strategic orchestrators of talent. In 2025, enterprises and businesses require scalable, intelligent, and automated AI agents to keep pace with their dynamic global expansion demands. What is the Role of AI Agents in Recruitment? AI agents in recruitment represent a fundamental move from traditional keyword-driven candidate search methods to AI agents that bring cognitive capabilities. These agent capabilities mirror human intelligence while operating at speed, accuracy, and scale. These AI systems understand context, recognize patterns, and make intelligent decisions analyzing complex, and multi-dimensional criteria to shortlist profiles. Unlike rule-based old systems, AI agents adapt their candidate matching algorithms based on real-time outcomes, becoming more accurate and effective as they learn over time to shortlist profiles. This learning capability is particularly valuable for enterprises, multinationals, and GCCs, which often need to fill highly specialized roles with shorter timelines. Transformative Role of AI Agents in Recruitment In 2025, AI agents will be deeply embedded across the entire recruitment lifecycle, performing a myriad of intelligent tasks such as: Semantic Job Description Analysis: AI agents will go beyond keyword matching, semantically analyze job descriptions, understand the context, experience levels, etc. and shortlist the right candidates with high matching scores. Intelligent Profile Matching: AI agents scan internal and external talent pools (ATS, LinkedIn, and GitHub, niche forums, etc.) to identify candidates whose profiles contextually align with job requirements, making intelligent matching. Automated Scoring & Ranking: AI agents automatically score and rank candidates based on their overall role fit, significantly reducing the manual effort of screening. Recruiters will receive a pre-vetted, high-quality shortlist, freeing them to focus on better candidate engagement. Automated Summaries & Justifications: AI agents generate natural language summaries explaining why a candidate is a strong fit enabling short summaries on their fitment suitability. Chatbots & Virtual Assistants: Intelligent chatbots handle initial candidate queries 24/7, providing instant answers about job roles, company culture, and application status. These agents personalize communication, guiding candidates through the application process, ensuring an improved candidate experience. Interview Scheduling: AI agents automate the complex task of scheduling interviews across multiple time zones, sending reminders, and coordinating with hiring managers and candidates, reducing no-shows and administrative burden. AI-driven Assessments: For technical roles, AI agents will administer and evaluate coding and technical assessments, and even simulations, providing objective candidates’ performance data. Behavioral & Cognitive Analysis: AI agents assist in analyzing recorded video interviews for candidates communication patterns, emotional intelligence cues, and cognitive abilities, providing insights to recruiters, without human biases. Real-time Market Insights: AI agents monitor talent markets to provide thought leadership with strategic insights for workforce planning and compensation strategies. Predictive Analytics: AI agents predict future talent needs based on project pipelines, technology trends, and attrition patterns, empowering enterprises to proactively build talent pools. What are the Benefits of AI-driven Recruitment for Enterprises The implementation of AI agents in recruitment represents a paradigm shift from reactive to proactive talent acquisition powered with cognitive thinking capabilities. AI agents excel at what traditional recruitment tools struggle by understanding the nuanced requirements of complex technical roles, identifying the right candidates for the right job role. Some of the benefits with AI agents in recruitment include: Accelerates time-to-hire Scalability and higher quality of hire Enhances recruiter productivity with more focus on candidate experience Bias reduction Real-time market intelligence and market insights Predictive analytics for strategic workforce planning Cost efficiency Data Privacy and Compliance Aspects to Ponder While AI agents bring immense value, but enterprises need to evaluate the below aspects: Data privacy & compliance: Proper understanding and complying with GDPR, local data laws, and other policies should be taken into account. Bias in algorithms: Continuous monitoring is required to prevent hidden biases in AI models. Change management: Recruiters need training to work alongside AI agents effectively. Candidate perception: Striking the right balance between automation and human touch is the need of the hour for businesses in 2025. The Future Outlook of AI Agents in Recruitment in 2025 In 2025, AI agents will be deeply embedded across the entire recruitment lifecycle, performing a myriad of intelligent tasks such as: Semantic Job Description Analysis: AI agents will go beyond keyword matching, semantically analyze job descriptions, understand the context, experience levels, etc. and shortlist the right candidates with high matching scores. Intelligent Profile Matching: AI agents scan internal and external talent pools (ATS, LinkedIn, and GitHub, niche forums, etc.) to identify candidates whose profiles contextually

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