Project Category: Case Study
Redefining Customer Support With a Custom AI Ticketing System
Challenges:
Manual email triage and ticket routing led to a time-consuming, inconsistent, and error-prone process, often leading to misclassified or delayed tickets.
Industry
Electronics
Solutions:
A Custom AI-Powered Ticketing System
Results:
80% reduction in manual handling time, a 40% boost in agent productivity, and significantly faster issue resolution with automated email parsing, ticket creation, and intelligent routing.
Location:
Japan
About the Client
The client is a global technology and electronics corporation employing over 110,000 people across 40+ countries. The company delivers solutions in IT infrastructure, communications systems, and enterprise services for both commercial and government customers. Its global support organization handles tens of thousands of service requests monthly, spanning enterprise servers, network devices, and digital transformation solutions.
The client’s customer support teams managed inquiries through multiple shared inboxes, relying on manual review and entry into a legacy helpdesk platform. This process was time-consuming, inconsistent, and error-prone, often leading to misclassified or delayed tickets. Support managers had limited visibility into workloads and response times, creating uneven agent utilization and frequent SLA breaches.
Case Overview
Skilled agents were spending significant time on administrative triage instead of problem resolution. The result was slower responses, higher operational costs, and declining customer satisfaction across key business lines — prompting leadership to explore automation as a path to improved efficiency and service quality. When evaluating solutions, the client’s leadership considered two primary options: expanding their support workforce or adopting a commercial ticketing tool. Both were quickly ruled out.
Adding staff would increase costs, and off-the-shelf ticketing software lacked the required intelligence. Stakeholders from Customer Support and IT identified that the real bottleneck lay in the manual interpretation and routing of incoming emails. Fingent proposed a Custom AI Ticketing System capable of understanding message intent, categorizing issues, and routing them instantly.
CHALLENGES
Roadblocks Faced In The Existing Systems
Manual email triage and ticket routing.
Time-consuming and error-prone processes.
Excessive manual efforts. Reduced productivity.
Misclassified and delayed tickets.
Reduced customer satisfaction.
SOLUTION
Fingent’s Approach - A Custom AI-Powered Ticketing System
The custom AI Ticketing System built on Microsoft .NET, PowerApps, and Azure AI Services is tightly integrated with the client’s in-house platform. The solution automates the entire lifecycle of incoming customer inquiries — from email receipt to ticket creation and intelligent routing — eliminating manual handling while ensuring precision, visibility, and compliance across the support organization. The AI Ticketing System is orchestrated through an agentic architecture. Distinct agents handle specific reasoning tasks.
The AI layer functions as a distributed agentic system operating within the client’s Azure environment.
Running on a serverless architecture using Azure Functions and Event Grid, the system scales automatically to process high email volumes in near real time.
All agent interactions are captured within a central observability stack powered by Azure Monitor and Application Insights, providing end-to-end transparency, compliance, and explainability.
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IMPLEMENTATION JOURNEY
Ensuring a Successful and Smooth AI Transition
The project was executed in a phased approach to ensure accuracy and user adoption. It began with a four-week pilot program focused on a single department with a high volume of structured inquiries. During this phase, we measured the AI's performance on categorization accuracy and the time saved per ticket. The pilot was an immediate success, demonstrating high accuracy in ticket categorization and proving the system's ability to handle high email volumes.
This early win built significant momentum and confidence among stakeholders. Following the pilot, the system was rolled out to subsequent departments over two months. Fingent conducted comprehensive training sessions for administrators and support agents, familiarizing them with the new automated workflows and the powerful reporting features of the Admin and Agent dashboards. One key challenge was ensuring the AI agents understood domain-specific product terms and acronyms that frequently appeared in support emails. Another challenge was latency during peak load.
Instead of model retraining, Fingent introduced a lightweight terminology-mapping module powered by a curated reference database.
This improved interpretation accuracy without modifying the base LLMs.
Event-driven batching and asynchronous queue management were implemented to resolve latency during peak load.
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BENEFITS
Making An Impact On Client Success
The AI Ticketing System architecture employs a modular, service-oriented design, integrated via secure REST APIs with internal tools, identity management systems (AD), and reporting interfaces. Built within the client’s private Azure infrastructure, the solution adheres to enterprise-grade governance, security, and data retention standards, ensuring robust performance, auditability, and operational resilience. Although the system has recently been deployed, the anticipated impact based on pilot results and performance projections is transformative. The company anticipates a dramatic reduction in manual labor and a surge in productivity.
Efficiency Improvement: Time spent on manual ticket creation, categorization, and routing will be reduced by up to 80%.
Faster Resolution Times: Time-to-resolution is expected to decrease significantly, directly boosting customer satisfaction.
Increased Productivity: Expected to increase overall agent productivity and capacity by an estimated 40%.
Enhanced Transparency: The new Admin Dashboard provides managers with real-time visibility into ticket volumes, agent activity, and more.
Reduced manual efforts
Boosted agent productivity
Faster & simplified processes
Enhanced customer experience
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How a Global Faith-Driven Media Conglomerate Modernized Its Tech Stack without Disrupting Operations
Challenges:
Overhaul the legacy system without disrupting ongoing operations to future-proof the website and mobile app of a religious media conglomerate.
Industry
Media/Nonprofit
Solutions:
Transformed a legacy platform into a scalable, future-ready digital platform for website and mobile app.
Results:
A modern, scalable, and future-ready tech stack that will support the future growth of the app across mobile and web platforms.
Location:
US
About the Client
One of the largest U.S.-based faith-driven media conglomerates known for producing faith-based, educational, and inspirational content across television, radio, and digital platforms. They wanted a website and mobile apps (Android & iOS) to help children learn timeless moral values and life lessons through engaging, Bible-based animated stories.
The vision was to bring the scriptures to life for families worldwide through an interactive adventure series featuring two time-traveling children and their robot companion. This concept quickly gained popularity and raked up 10 Million+ downloads in Android and iTunes app stores.
Case Overview
Fingent collaborated with the global faith-based media organization to modernize the tech stack without disrupting existing app services, ensuring uninterrupted access for users throughout the transition.
Both the backend and frontend architecture were replaced and upgraded to the latest version of a carefully assembled tech stack.
The project resulted in a complete overhaul of a near-obsolete legacy system, replacing it with a secure, scalable, and future-ready platform built to support over 3 million users while setting the ground for future expansion.
CHALLENGES
Pain Points in Existing Architecture
Upgrading the architecture without disrupting ongoing operations
Legacy mobile tech stack no longer supported
Urgent need to modernize the architecture to sustain growth
Complex CMS upgrade of large volume, structure, and content organization.
SOLUTION
Modernizing a Legacy System for a Global Faith-Based Media Organization
Fingent modernized the client’s existing platform to deliver a scalable, secure, and high-performing solution capable of handling over 20,000 concurrent users. The project emphasized modernizing the obsolete tech stack, a complete revamp of backend architecture, optimizing app performance, and ensuring uninterrupted access during the transition.
Complete backend revamp with federated GraphQL via WunderGraph, Azure Functions for serverless scaling, and multi-layered caching
Expanded capabilities in Node.js and native app development for a superior user experience
Implemented a SmartClient architecture to enhance flexibility and responsiveness
Conducted high-scale performance testing using Azure Load Testing to ensure stability for up to 20,000 concurrent users
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BENEFITS
Creating Lasting Value Through Modernization
The modernization initiative transformed the client’s platform into a modern, high-performing, future-proof, secure, and scalable solution. By enhancing system efficiency and ensuring uninterrupted service, Fingent helped the organization sustain its current audience while building a scalable foundation to expand its global reach further.
Maximized scalability and improved performance to support peak user volumes
Extensive testing across different call types, agent performance levels, and edge cases ensured reliable operation before deployment.
Streamlined data flow and elimination of data silos through Wundergraph API integration
Seamless migration with zero downtime during the architecture transition
Improved security and maintainability through the Drupal 10 upgrade
Enhanced system capability to handle 20,000+ concurrent users
Disruption-free transition
Future-ready architecture
Unified digital ecosystem
Rigorous load testing
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Transforming Call Center Quality Assurance with AI-Powered Automation
Challenges:
- Quality Assurance evaluated only 3% of 9400 daily calls across 350 agents.
- Lack of visibility into widespread performance trends and compliance risks. Inability to identify customer satisfaction drivers.
- The manual QA process consumed 2,550 hours monthly.
- QA operational cost estimated at $0.7 million annually.
Industry
Media/Nonprofit
Solutions:
AI-Powered Call Center Agent Scoring Application
Results:
Evaluation coverage from 3% to 100% of all calls. Complete visibility of agent performance across all interaction types, times, and customer segments. Quick identification of performance gaps and training needs. Reduced manual efforts and operational costs.
Location:
US
About the Client
The client is a diversified media organization with an annual revenue of approximately $700 million and a workforce of over 1,500 employees. Operating across content production, broadcasting, digital media distribution, and publishing, the company serves millions of customers worldwide. With a strong focus on innovation, it continuously adapts to emerging technologies to stay competitive in the dynamic media landscape.
The organization's call center handles an average of 9400 calls daily, through 350 customer service agents. However, their quality assurance program faced significant limitations. With only 12 dedicated QA agents, they could evaluate approximately 280 calls per day, which was just 3% of total call volume. This sampling bias prevented an accurate assessment of agent performance. Critical compliance issues or customer experience problems went undetected in the remaining 97% of calls. Coaching interventions were delayed by weeks due to insufficient data coverage.
Case Overview
The manual QA process consumed approximately 2,550 hours monthly, costing an estimated $0.7 million annually in QA labor alone. More critically, the organization lacked visibility into widespread performance trends, compliance risks, and customer satisfaction drivers. This limited their ability to proactively improve service quality and agent performance.
The organization's leadership evaluated several alternatives, including hiring additional QA staff, implementing traditional call monitoring software, and using off-the-shelf solutions. However, these approaches would scale costs linearly due to the fundamental limitation of sampling-based evaluation. Moreover, off-the-shelf software could not provide the quality and specificity of evaluation. The decision to pursue AI-powered automation was driven by the technology's unique capability to evaluate 100% of interactions while maintaining consistent scoring standards.
CHALLENGES
Roadblocks Faced In The Existing Systems
Inability to accurately assess agent performance
Limited QA coverage –only 3% calls were reviewed
Major compliance and customer experience issues went undetected
High manual QA efforts and costs –2,550 hours monthly, costing an estimated $0.7 million annually
Limitations to proactively improve service quality and agent performance
SOLUTION
Fingent’s Approach - AI-Powered Call Center Agent Scoring Application
Fingent implemented a comprehensive AI-based Call Center Agent Scoring Application built on Microsoft Azure's cloud-native serverless architecture. The solution utilizes Azure Functions and Azure OpenAI Service with multiple GPT models for intelligent cost-optimized processing. The system processes call transcripts through a sophisticated pipeline featuring pre-processing modules for text normalization and PII removal.
Advanced model-specific prompt engineering utilizing few-shot in-context learning with domain-specific knowledge injection is used to transform the organization's 10-criterion scorecard into structured evaluation instructions. Call ingestion occurs via REST API integration with the existing contact center platform. This triggers workflows that orchestrate evaluation through configurable sampling strategies—from targeted call types to full 100% coverage— for cost optimization.
The platform integrates bidirectionally with existing systems and business intelligence tools.
Comprehensive audit trails, data protection, and retention policies ensure compliance.
The system maintains enterprise-grade security with role-based access control and integration with Azure Active Directory.
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IMPLEMENTATION JOURNEY
Ensuring a Successful and Smooth AI Transition
The implementation began with a comprehensive feasibility study involving detailed analysis of the organization's existing QA criteria, call types, and evaluation processes. The team conducted extensive testing of different evaluation strategies and LLM models to identify optimal performance and cost. The development phase focused on building the core Azure-based application backend, the PostgreSQL, and the CosmosDB. This was done by establishing secure API connections with their existing contact center platform for automated transcript ingestion.
The rollout phase began with a pilot program covering specific call types and agent groups, gradually expanding to full deployment across all 350 agents. This phase involved fine-tuning based on real-world performance data, optimizing cost efficiency and processing strategies, and training managers and QA staff on the new system capabilities. Throughout the implementation, Fingent maintained a strong stakeholder engagement with regular progress updates, addressed technical challenges through iterative problem-solving, and ensured seamless integration with existing workflows to minimize operational disruption.
Developed a React-based user interface for QA supervisors and managers to review and modify AI evaluations.
Extensive testing across different call types, agent performance levels, and edge cases ensured reliable operation before deployment.
Implemented comprehensive audit trails and integration with Power BI for reporting.
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BENEFITS
Making An Impact On Client Success
The AI-powered QA solution delivered improvements across multiple dimensions of the organization's call center operations. The most significant achievement was expanding evaluation coverage from 3% to 100% of all calls—a massive increase in visibility. This comprehensive coverage eliminated sampling bias and provided the first complete picture of agent performance across all interaction types, times, and customer segments. The automation generated substantial cost savings by reducing manual QA labor requirements significantly. QA staff can now focus on strategic coaching, trend analysis, and complex cases requiring human judgment.
Coaches and managers gain access to comprehensive, objective performance data enabling fair and accurate agent assessments.
Data-driven decisions can be made about training programs, coaching priorities, and performance management.
The system provides the base to identify performance patterns, compliance risks, and improvement opportunities at scale
Real-time alerts for compliance violations and quality issues enable immediate intervention.
Quick identification of issues
Strategic, data-driven training approaches
Reduced QA operational costs
Improved service quality & agent performance
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Unlocking Marketing Intelligence with a Conversational AI Agent
Challenges:
The marketing team lacked access to actionable intelligence from 9,400 daily call center interactions. This limited their understanding of their customer behavior and changing trends.
Industry
Media/Nonprofit
Solutions:
Custom Conversational AI Agent to unlock marketing intelligence
Results:
Accelerated campaign development by 3 weeks. Faster intelligence gathering process. Enhanced clarity on customer needs and behavior. Data-driven and targeted marketing campaigns. Improved user engagement and brand trust.
Location:
US
About the Client
The client is a diversified media organization with an annual revenue of approximately $700 million and a workforce of over 1,500 employees. The company operates across multiple verticals, including content production, broadcasting, digital media distribution, and publishing, serving millions of customers worldwide. The company focuses on evolving with emerging technologies to stay competitive in a rapidly changing media landscape.
The company handles approximately 9,400 call center interactions daily. It's equivalent to 3.4 million annual conversations' worth of untapped user feedback. However, the marketing team had no scalable method to analyze these conversations. This left them unable to identify trends, measure sentiment, or understand the specific needs and motivations of their users, despite having an excellent data source at hand.
Case Overview
The lack of insight was a significant opportunity cost. Marketing campaigns were designed with incomplete information, limiting their effectiveness and ROI. Product development was reactive, relying on delayed or indirect feedback rather than real-time data. Crucially, the organization was missing the ability to proactively understand its users, which is essential for building loyalty and driving engagement in a competitive media landscape.
The leadership evaluated expanding customer surveys and manual call analysis teams. But surveys yielded low response rates, while manual analysis was unscalable and too expensive. Fingent recognized that an AI Agent-based solution can analyze 100% of call data in real-time while leveraging existing infrastructure from a parallel call center agent evaluation project. The initial concerns about AI accuracy and implementation complexity were addressed through a phased PoC and MVP approach, which demonstrated quick wins during the design phase.
CHALLENGES
Roadblocks Faced In The Existing Systems
Inability to convert 3.4 million annual customer interactions into insightful data
Manual and weeks-long intelligence gathering process
Inability to proactively understand customer needs and behavior
Lack of data-driven campaign development
Inability to build brand loyalty and drive customer engagement
SOLUTION
Fingent’s Approach - Custom Conversational AI Agent
The conversational agentic AI system was built on a cloud-native serverless architecture using Azure Functions. The solution features a React-based conversational chat interface embedded into the client's internal applications for easy access. Marketing users query the data using natural language, which is then routed to AI agents equipped with specialized tools.
The stack is designed for modularity and scalability, leveraging Python’s async capabilities for low-latency API interactions, paired with a responsive React-based front end. The serverless architecture using Azure Functions ensures efficient resource utilization. The system leverages PostgreSQL with pgvector to store embeddings for RAG ( Retrieval Augmented Generation), and a NoSQL store for unstructured data. The system includes a data pre-processing pipeline that embeds relevant data, stores metadata, and generates additional contextual information. The agentic AI system is powered by Azure OpenAI for planning and execution.
The system uses both REST APIs and MCP (Model Context Protocol) to access data and tools.
The architecture prioritizes scalability, fault tolerance, and extensibility.
Azure AD, RBAC, and additional security measures ensure enterprise-grade compliance.
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IMPLEMENTATION JOURNEY
Ensuring a Successful and Smooth AI Transition
Implementation began after validating the concept and identifying strategies to overcome key bottlenecks via multiple PoCs. The three-month project was structured across three distinct phases to ensure an agile and effective rollout.
Throughout the project, the team successfully navigated key challenges related to performance and data accuracy. To ensure fast response times when querying large volumes of data, the system's architecture was optimized for efficient data processing and caching. The reliability of the AI-generated insights was ensured by a validation process that combined automated confidence scores and source citations. This practical approach ensured the final tool was fast and reliable.
Phase 1 - Scope & Design: This initial phase of one month focused on identifying the core set of business questions, designing the agentic AI architecture, creating the data processing pipelines, and creating UI mocks.
Phase 3 - Pilot Rollout & Iteration: Early wins included successfully answering 78% of initial queries and reducing typical research tasks from hours to minutes.
Phase 2 - Development & Integration: The core development phase included building the React-based chat interface, developing the AI agent and its backend tools on Azure, establishing the data integration with their contact center platform, and finishing with a week of testing with various sample scenarios.
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BENEFITS
Making An Impact On Client Success
The solution transformed the marketing team’s intelligence gathering from a manual, weeks-long process to instant query resolution, accelerating campaign development time by 3 weeks. Estimated time savings averaged 85%, with some tasks that previously required over 4 hours of manual research now completed in under 15 minutes. This new capability led to several measurable improvements in marketing and product development operations.
Data-Driven Campaign Development: The team could now design campaigns based on a real-time understanding of user needs, concerns, and sentiment.
Efficient Content Generation: The ability to query the entire call database allowed for the rapid identification of powerful user testimonials and stories for use in content.
Accelerated Product Improvement Cycles: Direct, unfiltered feedback on products and services could be analyzed at scale to identify and prioritize enhancements.
Enhanced Regional Analysis: The marketing team could now perform demographic and regional analysis to create more user-specific strategies.
Improved customer engagement
Customer-specific marketing campaigns
Agile decisions in product strategy
Enhanced brand trust & loyalty
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Transforming Operations for a Leading Experiential Marketing Agency with Agentic AI
Challenges:
The inability to quickly access relevant client information and project details hampered the organization's efficiency in client and event management. The sales team was also losing potential deals.
Industry
Marketing
Solutions:
An Enterprise-Grade AI-powered Operational Assistant
Results:
70% reduction in routine information lookup workloads for sales and operations teams. A 40% reduction in the time needed to generate a client report. 75% reduction in time required to analyze project data. Sales productivity increased by 3-5%.
Location:
US
About the Client
The client is an award-winning experiential marketing company. They focus on building bold, immersive brand experiences across three core areas: experiential marketing & brand activation, event marketing & trade show services, and integrated marketing & live experiences. Approximately 3,000 people operate from eight strategic locations nationwide. The agency serves over 350 clients, including marquee Fortune 500 companies like Samsung and Google, organizing over 3,000 events annually worldwide.
The agency's rapid growth, however, created significant operational bottlenecks across their enterprise systems. Sales representatives found it complex and time-consuming to quickly find basic client contact information, past project details, and internal points of contact during time-sensitive client calls. Around 10-15 minutes were often wasted navigating multiple CRM screens, filters, and databases to derive such information.
Case Overview
Project managers also struggled to coordinate with resources across multiple simultaneous events. They often had to spend hours manually searching through various project databases to locate timelines, status updates, and resource assignments. The operations teams faced delays in event setup because they couldn't quickly locate specific inventory items, equipment, and materials across multiple warehouses and locations.
Fingent helped the client implement an enterprise-grade AI-powered operational assistant leveraging large language models (LLMs) with conversational AI capabilities. The AI solution enabled the team to quickly access client history, project data, and inventory information. A 3-5% increase in sales productivity, a 40% reduction in report generation time, and a 25% improvement in customer satisfaction through faster, more informed client responses were achieved through the solution.
CHALLENGES
Roadblocks Faced In The Existing Systems
Manual processes demanded significant time and resources.
Inability to quickly access the client history during critical sales calls.
Reduced speed. Delayed responsiveness.
The sales teams were losing potential deals.
Complex client relationship management. Slow revenue generation.
SOLUTION
Fingent’s Approach - Enterprise-grade AI-powered Operational Assistant
The enterprise-grade AI-powered operational assistant deployed a cloud-native microservices framework with API-first design patterns. This helped create a unified conversational interface layer that orchestrated data access across the agency's three core business systems: their CRM platform (containing client contact information and communication history), project management system (storing timelines, assignments, and status updates), and inventory management platform (tracking equipment, materials, and props across all locations).
The technical architecture utilizes a retrieval-augmented generation (RAG) approach that grounds AI responses in real-time enterprise data from connected systems. An intelligent query orchestration engine employs multi-step reasoning with chain-of-thought prompting. This helps decompose complex natural language queries into discrete API calls across heterogeneous data sources.
The system maintained enterprise-grade security through AD SSO, role-based access control (RBAC), and end-to-end encryption for all data transactions.
The solution helps achieve 96.4% semantic accuracy in cross-platform information synthesis and retrieval.
Advanced prompt engineering with few-shot learning examples enabled domain-specific query understanding.
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IMPLEMENTATION JOURNEY
Ensuring a Successful and Smooth AI Transition
The implementation journeys involved a three-month development and integration phase, one month of testing and soft launch, plus another month to enable organization-wide rollout. The technical implementation commenced with comprehensive API discovery and schema mapping across all target systems, with REST API wrappers for standardized interfaces to legacy systems. During this period, the development team worked closely with department stakeholders to understand specific workflow requirements and ensure the natural language processing could handle industry-specific terminology and complex multi-system queries. Custom integrations were built to maintain data security and real-time synchronization across all platforms.
A controlled staging environment replicated production infrastructure for comprehensive testing with selected power users. Performance benchmarking measured query latency, throughput, and accuracy metrics using automated testing frameworks. Testing included adversarial testing for prompt injection vulnerabilities, data leakage prevention, and edge case handling. Custom and cloud native monitoring systems provided real-time observability into system performance, token consumption, and user interaction patterns. Early wins included dramatic improvements in information retrieval speed and positive feedback from pilot users who noted the intuitive nature of the natural language interface.
The full deployment phase included comprehensive change management support with self-learning materials and training videos to ensure smooth adoption across all 1,000 employees.
Ongoing support was provided throughout the rollout period to address any adoption challenges and ensure maximum utilization of the new system.
Department-specific customizations were implemented based on pilot feedback, including specialized query templates for common sales scenarios, project management workflows, and inventory searches.
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BENEFITS
Making An Impact On Client Success
The AI-powered operational assistant delivered substantial improvements in day-to-day operations across all departments. The agency achieved a 70% reduction in routine information lookup workloads for sales and operations teams. It transformed tasks that previously required navigating 5-8 different screens into simple natural language queries.
Project teams experienced a 40% reduction in time needed to generate client reports and project summaries, while operations saw a 75% reduction in time required to analyze project data and resource allocation across their 3,000 annual events. Sales productivity increased by 3-5% as representatives gained faster access to client history during critical sales calls, leading to more informed conversations and improved deal closure rates.
85% of employees started fully utilizing the AI assistant within three months of the full rollout.
The natural language interface eliminates the frustration of complex multi-system searches.
The system now handles 80% of routine information requests that were previously managed through manual system navigation.
Users can now focus on higher-value client service activities rather than administrative tasks.
Improved operational efficiency
Reduced manual efforts. Faster processes
Improved sales perfromance
Enhanced competitive advantage
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Transforming Lead Response with AI Agent Automation
Challenges:
Time-consuming manual sales processes resulted in slow responses to leads and loss of sales opportunities.
Industry
IT
Solutions:
Custom AI agent to automate lead identification, lead routing, and personalized responses.
Results:
Response time reduced from 24+ hours to a consistent sub-one-hour standard. Reduced manual lead processing efforts. Improved productivity and efficiency.
Location:
US
About the Client
The client is a mid-sized B2B technology consulting and system integration service company. With over 500+ employees, the firm serves global customers across multiple time zones. The company typically receives 50+ inbound leads daily through their primary inbox, which is handled by 15+ dedicated sales managers of various expertise in different time zones.
The company, however, faced significant bottlenecks in their lead management process. They followed a complete manual workflow involving dedicated staff to constantly monitor emails and identify genuine leads. Identified leads were forwarded to sales managers as bulk emails. These were then segregated and nurtured by respective sales managers. The process was extremely slow. It increased the chances of missing out on opportunities. The entire process hampered lead response time and quality, ultimately resulting in loss of leads.
Case Overview
Manual lead processing was becoming a significant competitive disadvantage for the firm. They estimated losing 30-40% of potential leads to competitors who responded faster. Moreover, the group forwarding approach led to accountability issues. Some leads received multiple responses, while others were overlooked. Sales managers often provided rushed, generic responses that failed to convert. Capturing accurate data on CRM was also turning difficult, hampering effective follow-ups.
The company initially considered hiring additional staff and implementing email forwarding rules. However, they quickly realized that this wouldn’t improve response speed, personalization, or data accuracy. The company then considered an AI approach. They started off with a comprehensive pilot program to test email quality and proper lead routing. The AI agent is now utilized to generate professional, personalized responses while maintaining 100% accuracy in sales manager assignments.
CHALLENGES
Roadblocks Faced In The Existing Systems
30 to 40% of deals were lost due to slow sales processes
Need to keep dedicated staff on the job, round the clock, and on weekends
Delayed or rushed lead responses
Difficulty in capturing accurate data of prospects in the CRM
The manual and slow sales process posed a significant competitive disadvantage
SOLUTION
Fingent’s Approach - Custom AI Agent Automation for Lead Response
Fingent implemented a sophisticated AI agent-based solution built on a node-based workflow automation architecture. The solution utilizes JavaScript runtime engines and Python execution environments, with OpenAI's GPT language models and LLM capabilities. The core classification engine leverages contextual frameworks to distinguish between genuine leads, partnership opportunities, HR inquiries, and other correspondence with 96% accuracy.
The system implements a human-in-the-loop fallback mechanism for edge cases. The routing engine executes conditional logic by interfacing with the company's territory assignment database through REST API calls. It utilizes dynamic rule-based assignment algorithms to factor geographic regions and industry expertise across the 15+ sales managers.
Adapts the tone and content depth of prospects to generate personalized communications.
Allows syncing CRM via webhooks and scheduling emails with calendar integrations.
Allows encrypted secret management and role-based access controls for enhanced security.
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IMPLEMENTATION JOURNEY
Ensuring a Successful and Smooth AI Transition
To enable a smooth transition, the implementation process began with a three-week pilot phase involving only two sales managers from different domains. This phase focused on fine-tuning email classification and personalizing responses. Key technical challenges like email parsing, lead identification, and eliminating false positives were addressed with a human-in-the-loop. The most significant challenge was to accurately analyse complex emails that did not have a clear intent specified in the messages.
The pilot phase was, however, a success. Although there was initial skepticism about AI-generated communication, the quality of lead responses proved reliable. User adoption was also seamless. It hardly required any process change as the system was integrated directly into the existing email process workflow. Full deployment to all 15+ sales managers was completed within two weeks with comprehensive monitoring and real-time performance optimization.
A structured eight-week approach to enable a smooth transition.
Addressed critical technical challenges with a human-in-the-loop approach.
System directly integrated into the existing workflow eased user adoption.
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BENEFITS
Making an Impact on Client Success
The AI agent implementation delivered immediate and measurable improvements. Response times dropped from the previous 4-24+ hour range to a consistent sub-one-hour standard. Most prospects received personalized responses within 30 minutes of their initial inquiry. The system achieved 96% accuracy in lead identification. The remaining 4% lead is successfully captured by the human-in-the-loop backup process. This results in zero lost opportunities due to classification errors.
100% accuracy in sales manager assignment
Prospect data is automatically populated in the CRM system
Manual lead processing managers are freed up to focus on higher-value activities
Improved the quality of follow-up conversations
Improved lead qualification rates
Improved data capturing abilities
Consistent and personalized responses
Enhanced company brand perception
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SAP Ariba Optimization for a Leading Healthcare Sector
Challenges:
SAP S/4HANA integration issues that limited reach to the supplier network. Data silos, annual tracking, and reporting affected operational efficiency.
Industry
Healthcare
Solutions:
SAP Ariba Optimization
Results:
Enhanced system controls & compliance, clearly defined SOD’s, automated management approval workflows, and wider supplier collaboration.
Location:
Saudi Arabia
About the Client
Johns Hopkins Aramco Healthcare (JHAH) is a premier healthcare provider in the Kingdom of Saudi Arabia. The organization is dedicated to serving the Saudi Aramco employees and their dependents with high-quality, patient-centered care.
In order to maintain operational excellence, the client wanted to modernize their supplier lifecycle and performance management using advanced technologies. However, they were encountering SAP Ariba implementation and S/4HANA integration issues.
Case Overview
Fingent implemented a tested and proven solution framework to address the client's challenges. The engagement began with a comprehensive assessment of the current systems in use, followed by clearly defined milestones and governance protocols.
The project was initiated with a strong foundation of stakeholder alignment. This was facilitated through the establishment of robust project governance structures and a detailed RACI matrix outlining roles and responsibilities.
CHALLENGES
Roadblocks Faced in the Existing System
SAP Ariba–S/4HANA integration issues
Offline communication and data silos
Limited reach to the supplier network
Manual tracking and reporting
Low adoption of SAP Ariba
SOLUTION
Fingent’s Approach - SAP Ariba Optimization
Fingent implemented structured change management and communication strategies to drive organizational buy-in and support user adoption. This included regular engagement with key stakeholders and communication of project benefits.
Training plans were designed to demystify SAP Ariba’s capabilities
clearly document AS-IS and TO-BE process models
Developed comprehensive training manuals and documentation
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SOLUTION BENEFITS
Making an Impact on Client Success
The seamless integration with SAP S/4HANA brought end-to-end transparency and real-time data flow across the organization. Collaboration with suppliers was significantly enhanced via the Ariba Business Network, while automated dashboards and reports delivered real-time insights to support faster, data-driven decisions.
Clearly defined SOD’s, and automated management.
Wider supplier collaboration through Ariba Business Network
Single source of information enabling friction-free audits
Automated analytical reports & dashboards to provide real-time insights
Quick decision-making
Effective data utilization
Improved collaboration
Efficient processes
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Empowering Data-Driven Decisions with Intuitive Dashboards
Challenges:
Intuitive Dashboards for Data-Driven Decisions
Industry
Logistics
Solutions:
Using Tableau and Grafana to create an intuitive dashboard for a quick overview of useful metrics
Results:
Quick identification of operational gaps. Faster anticipation of market changes and development of strategic solutions to ensure an agile and future-proof business.
Location:
New Zealand
About the Client
Dynes Transport is a leading transport company based in South Island, New Zealand. The company specializes in providing efficient and reliable logistics solutions. With a strong presence in the dairy, forestry, and broader transport sectors, the client delivers end-to-end supply chain services tailored to industry-specific needs.
Although Dynes strives to offer enhanced logistics services, demand fluctuations often challenge them. They realized that proper planning and utilization of trucks could help them provide better services.
Case Overview
The need of the hour was to make quick decisions using insightful data. The client already utilized multiple applications to source insightful information about truck maintenance, driver availability, customer demand changes, and more.
However, they lacked a consolidated view and a better representation of all the raw data that could support quick decisions. Fingent proposed creating Intuitive Dashboards for the client that could provide them with insightful data at a glance.
CHALLENGES
Roadblocks Faced in the Existing System
Use of multiple applications delivered disparate data
Lack of unified view of consolidated data
Limited access to real-time data
Inability to make quick and intelligent decisions
Lack of proper planning and truck utilization
SOLUTION
Fingent’s Approach - Intuitive Dashboards for Data-Driven Decisions
Fingent collaborated with the client’s in-house tech team to understand their data patterns, KPIs needed for intelligent decisions, applications in use, and workflow structure. Comprehending all these factors, Fingent proposed creating Intuitive Dashboards for the client that could provide them with insightful data at a glance.
Intuitive dashboards to provide a quick view of key KPIs
Implementing Power BI can help crunch large volumes of data
A data warehouse using Microsoft’s Azure Synapse to structure raw data
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SOLUTION BENEFITS
Making an Impact on Client Success
The intuitive dashboards developed for the client serve as a centralized hub for collecting, consolidating, and analyzing critical business data from various applications. The solution provides visually insightful graphs and performance metrics by integrating data across multiple operational facets.
Quick view of key KPIs for intelligent and quick decision-making
Securely stored structured and consolidated data
Proper planning and enhanced operational efficiency
A secure, scalable, and future-ready solution
Easy data management
Consolidated view of
operations
Enhanced decision-making
Cost optimization
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Smart Home Automation System Redefining Living Experiences
Challenges:
Improve home comfort and personalization, transform living with smart technology, and offer unique customer experiences
Industry
Real Estate
Solutions:
Smart Home Automation System
Results:
15% increase in property value, improved home security, energy saving, centralized home management, flexibility to integrate new devices, enhanced comfort, and personalization.
Locations:
UAE
About the Client
The client is a prominent real estate management company in the Middle East with a renowned reputation of over 25 years. The company manages a vast portfolio of 10,000 properties and aims to create exceptional customer experiences.
The client team specializes in providing comprehensive, end-to-end property management solutions that cater to property owners' diverse needs. Committed to building trusted and valued brands in the real estate sector, the company leverages streamlined collaboration, innovative strategies, and cutting-edge technologies.
Case Overview
In a world of smart devices and connected solutions, home automation is emerging as a game changer for builders and homeowners. The seamless integration of various devices is redefining home interactions and setting new standards for modern living.
Fingent helped design a home automation system compatible with popular voice assistants like Alexa and Google Assistant, offering users the convenience of hands-free control. This integration ensures a user-friendly experience and enhances the overall functionality of the home automation solution, making it truly innovative for the client's customers.
CHALLENGES
Roadblocks Faced In The Existing Systems
Improve home security
Improve home comfort and personalization
Enable energy efficiency
Enable centralized control
Transform living with smart technology
SOLUTION
Fingent’s Approach - Smart Home Automation System
The home automation system integrated smart devices like lights, thermostats, door locks, and security cameras to improve living experiences. The system also interacts and exchanges information using wireless protocols. Users get the ease of operating the system through their mobile devices.
Smart door locks for a new level of security and convenience
Connecting the garage lock to the tenant app helps secure vehicles
Smart intercoms allow tenants to receive visitors remotely & verify identities
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BENEFITS
Making an Impact on Client Success
Smart home technologies are the future of real estate. Properties equipped with home automation see a 15% increase in market value and attract more potential buyers! Interacting with and controlling homes from anywhere is a groundbreaking innovation. With enhanced convenience, improved security, and effective energy savings, smart home technology is boosting property value.
A user-friendly smartphone app to control and customize smart devices
Connected smart devices over wireless protocols to enhance home experience
Each smart device features - sensors, actuators, and connectivity options.
Homeowners can use the mobile app to create automation routines
Unique customer experience
Energy saving
Improved home security
Flexibility to integrate new devices
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Custom Property Management Solution For Modern Realtors
Challenges:
Improve property management, make data-backed decisions, elevate the living experience for tenants
Industry
Real Estate
Solutions:
An Integrated Web App for Property Management Mobile App to Ease Tenant Payment and Communication
Results:
Improved property management, eased staff management, simplified tenant management, streamlined dashboards and reports
Locations:
UAE
About the Client
The client is a prominent real estate management company in the Middle East with a renowned reputation of over 25 years. The company focuses on redefining living experiences through innovative property and lifestyle management. Merging the best of communities, property management services, and technology, the firm builds trusted and valued brands in real estate.
Managing a complex portfolio while maintaining market relevance demands modern approaches. Our client, managing over 10,000 properties, needed robust technology to streamline collaborations with property owners and stakeholders.
Case Overview
Property management encompasses various operations, including staff management, accounting, maintenance, and resident services. Understanding these facets, Fingent provided two customized solutions that empowered tenants and property managers equally.
Fingent’s two-faceted solution included a web application for the admins and landlords to track and manage properties effectively. The mobile app was developed for tenants to ease rent payments, maintenance requests, and more.
CHALLENGES
Roadblocks Faced In The Existing Systems
Lack of Proper Collaboration
Tedious property management tasks
Inability to generate useful data for stakeholders
Improve the living experience for tenants
Ease tenant management and rent collection
SOLUTION
Fingent’s Approach - Smart Home Automation System
Fingent proposed well-integrated custom-built software that empowered property owners and tenants to make data-backed decisions equally. The solution included a web application for admins and landlords to track and manage properties effectively and a mobile app for tenants to ease rent payments, maintenance requests, and more.
Staff management to monitor workloads and staff efficiency
Property management to track occupancy rates, handle leases, and more
Financial management to track income and expense
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BENEFITS
Making an Impact on Client Success
Effective property management requires prompt communication, effective tracking, and enhanced visibility. Fingent ensured the custom-built web application empowered clients and property owners with streamlined processes and rapid collaboration. The intuitive dashboards also equipped the team with quick insights for smart decision-making.
Tracking occupancy rates and handling lease agreements simplified
Managing property maintenance and repairs eased
Easy tenant onboarding and communication
Generating financial statements eased
Streamlined workflows
Simplified tenant management
Improved collaboration
Intelligent decision-making
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