Project Attribute: AI
Responsible AI Governance for the Agentic Era: A Risk-Based Framework for CXOs and IT Leaders
Agentic AI systems are already planning, deciding, and acting inside your organisation with less human intervention every quarter. As that autonomy grows, boards and regulators are converging on the same question: how is this being governed?
This white paper sets out a practical, scalable framework built on a simple premise: governance depth should match risk.
It's grounded in the Australian Government's Guidance for AI Adoption and New Zealand's principles-based approach, and reflects the transparency, human-oversight, and documentation themes of the EU AI Act. It gives organisations a credible operating foundation across all three markets.
What You'll Find in This White Paper
- A risk-based, three-tier model for scaling governance controls to match a system’s autonomy and impact
- A side-by-side comparison of the AU/NZ principles-led approach and the EU AI Act’s legally prescribed obligations — and where this framework fits each
- The six governance principles regulators and boards expect to see evidenced, from accountability to human control
- A clear breakdown of developer vs. deployer responsibilities, so accountability never falls into a gap
- A five-step practical starting point for building an AI Use Register from scratch
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What You'll Learn
Why applying the same governance checklist to every AI initiative slows down low-risk work without actually managing high-risk exposure
How to triage an AI system's risk tier using purpose, data sensitivity, autonomy, and stakeholder impact
What supply-chain transparency means in practice when your AI depends on third-party models and APIs
How to build human-centred design and redress into agentic systems
Why this framework is a foundation to build on, not a substitute for EU AI Act legal obligations like conformity assessment and CE marking
Key Take Away
Governance should be proportionate to risk — not a fixed checklist applied uniformly to every AI initiative, and not an afterthought bolted on after deployment.
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AI for SAP ECC: A Practical Path to AI Adoption
As AI reshapes business operations, organizations are looking for practical ways to apply it across their SAP environments. But for businesses still running SAP ECC, adopting SAP-native AI capabilities is not as straightforward as it is with S/4HANA Cloud.
The good news is that businesses do not have to wait for migration to start exploring AI. With the right architecture, organizations can begin capturing value from AI on ECC today while keeping their future S/4HANA plans in view.
Why It Matters Now
SAP ECC mainstream support ends on December 31, 2027. Many businesses are still evaluating their ECC-to-S/4HANA migration plans. This creates an important window to take a practical approach to AI.
What You’ll Find In This Piece?
- Where AI can realistically be applied inside an SAP ECC environment today
- Why native AI capabilities are constrained on ECC
- Four practical AI approaches and their value, effort, and risks
- Recommended paths for higher AI implementation success
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What You'll Learn
Why Joule is not available on ECC in the same way as S/4HANA Cloud.
How on-premises customers can access Joule by committing 50% of their maintenance spend to cloud-first services.
Use cases worth exploring and key to choosing the right architecture.
A two-stage bridge architecture that delivers value on ECC today.
How to capture AI value before SAP ECC mainstream support ends on December 31, 2027.
Key Take Away
AI on SAP ECC is possible!
The key is to connect near-term AI value to your future SAP environment.
The key is to connect near-term AI value to your future SAP environment.
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Modernizing HRM/Payroll Application with SaaS Multi-Tenant Architecture
Challenges:
The company’s legacy platforms limited their scaling opportunities. Performance optimization was also becoming difficult. The company wanted to make a shift to a truly cloud-first, future-ready platform.
Industry
Technology
Solutions:
Modernization with SaaS Multi-Tenant Architecture
Results:
The scalable SaaS foundation enables smooth business expansion while delivering improved speed, stability, and a better user experience. Its centralized, cloud-native architecture also helps reduce operational overhead and simplify management.
Location:
US
About the Client
The client is a well-established provider of HR and payroll solutions. They focus on helping businesses across the Caribbean to streamline and simplify their workforce operations. Their platforms are designed to improve efficiency, ensure compliance with local regulations, and make everyday HR processes like payroll management, work permits, and employee data handling more seamless.
The client utilized an HRM solution that was largely monolithic. It made it harder to scale, optimize, and fully leverage modern cloud capabilities. As their customer base grew, the company wanted a more scalable, efficient, and modern architecture. Although they initiated an app migration to a scalable Azure SaaS model, the transition was largely a lift-and-shift effort to preserve the legacy structures and functionalities.
Case Overview
With multiple deployment models still in play, maintaining consistency and efficiency became increasingly complex. The client was in need of a development team that could offer a structured approach to modernize the platform, resolve technical issues, and make the application truly cloud-first, scalable, and future-ready.
Fingent addressed the requirement through a streamlined approach, beginning with a comprehensive analysis of the existing SaaS architecture. The next move involved identifying gaps and prioritizing pending tasks. It was followed by building a product plan with clearly defined milestones to migrate 60% of the existing clients into the new cloud platform and enable continuous improvement.
CHALLENGES
Roadblocks Faced In The Existing Systems
Legacy platforms and outdated technology
Complex system architecture
Limitations to scale
Inability to optimize operations over time
Lack of a truly cloud-first, future-ready solution
SOLUTION
Fingent’s Approach - Modernization with SaaS Multi-Tenant Architecture
At the core, the solution involved optimizing the existing codebase. This included refining the legacy components and introducing asynchronous processing where needed. The focus remained on improving the overall system efficiency through a secure, scalable SaaS model powered by a multi-tenant Azure architecture.
To support scalability, the application was further aligned with Azure PaaS capabilities. A strategic migration of the environment to the Canada Central region was also enforced to ensure better compliance and performance for regional users.
Focus on modernizing the user experience
SaaS Go-live and migration plan ensured a smooth transition
Building future-ready capabilities enabling subscription-based billing models
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BENEFITS
Making An Impact On Client Success
With a strong foundation in place, the client is positioned to transition from a legacy-driven system to a modern, scalable SaaS platform. The optimized multi-tenant architecture and improved codebase are expected to significantly enhance system performance. With improved stability and user experience, the system can even support larger organizations with complex workforce needs. A centralized and cloud-native management reduces operational overhead while enabling faster onboarding of new clients. This boosts the company’s ability to smoothly expand into new markets.
Scalable SaaS foundation to support rapid onboarding and smooth expansion
Enhanced speed, stability, and overall user experience with code optimization
Region-specific hosting ensures better compliance and lower latency
Reduced operational overhead with a centralized, cloud-native management
Enhanced data security
Seamless expansion
Performance optimization
Cost optimization
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OCR Invoice Automation and Calculation
Challenges:
Manual ticket verification increased invoicing time. Detention tickets accounted for 20% of total daily orders.
Industry
Logistics
Solutions:
OCR Invoice Automation - AI-powered Ticket Time Extraction System
Results:
Increased autoinvoicing from 40% to 70% reduced manual interventions and processing delays, accelerating invoice completion and payment timelines.
Location:
US
About the Client
The client is a mid-sized transportation logistics company with an annual revenue of $20 million and an average of 10,000 orders per month. Operating across the mainland USA, the company partners with major oil refineries and other logistics providers to deliver comprehensive services to customers nationwide.
The company operated in a fast-paced logistics environment but continued to rely on physical paper tickets as proof of delivery and supporting documentation for invoicing. These tickets included handwritten timings, which were essential for calculating detention time at pickup and drop-off locations. Detention tickets accounted for 20% of total daily orders. The accounting team was required to manually verify these handwritten timings and apply the rate sheet to charge customers accordingly. This manual process created significant operational challenges. It required substantial labor. Extended billing cycles and delayed payments limited the team’s ability to process invoices efficiently.
Case Overview
The manual verification process consumed over 600 hours annually and delayed the issuance of detention invoices, impacting cash flow. During peak seasons, the growing volume of detention tickets further increased verification time. This extended invoicing cycles and added to manual labor requirements.
The company recognized that their customers would prefer to continue using physical tickets. Fingent thus proposed OCR Invoice Automation with AI-powered Ticket Time Extraction System that used Azure’s custom Document Intelligence model for handwriting recognition. This solution enabled the company to automate timing capture without changing processes for drivers or customers. The implementation reduced human errors, accelerated invoicing, and improved operational efficiency. Initial concerns about handwriting recognition accuracy were addressed by retraining the model with over 100 tickets, achieving an accuracy rate exceeding 95% for each ticket.
CHALLENGES
Roadblocks Faced In The Existing Systems
Excessive manual labor and cost.
Slow process. Extended billing cycles.
Frequent errors and inefficiencies.
Delayed invoicing and payments.
Delayed tickets accounted for 20% of total daily orders.
SOLUTION
Fingent’s Approach - A Custom AI-Powered Ticketing System
The AI-powered Ticket Time Extraction System for OCR Invoice Automation was developed using Microsoft Azure Document Intelligence. The solution automates the entire process—from document upload to data validation—ensuring accurate, consistent, and real-time extraction of key ticket details. Uploaded ticket images and documents are first classified through a Custom Document Classification Model, confirming standard formats before extraction.
Once validated, the system employs a Custom Data Extraction Model that leverages OCR and layout-based recognition to identify ticket timings, dates, and other essential fields from both structured and semi-structured documents. The extracted data is automatically transmitted back to the customer’s portal through secure API integration, where it is verified and matched against order details for accuracy and consistency.
Automated document classification and extraction using Azure Document Intelligence.
Intelligent calculation module ensures precision by automatically matching extracted timings with order details.
End-to-end integration with the customer portal for real-time validation and data synchronization.
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IMPLEMENTATION JOURNEY
Ensuring a Successful and Smooth AI Transition
The OCR Invoice Automation was implemented through four phases. It began with a detailed feasibility and design phase, where the existing automation process was analyzed to identify inefficiencies within the 59% automation baseline. Multiple OCR and vision models—including Azure Document Intelligence and Google OCR—were evaluated for accuracy and reliability. Based on this assessment, AI-driven calculation workflows were designed and aligned with order validation rules.
During the development and integration phase, the team built a robust document upload pipeline using Azure APIs and developed a document identification model for standardized invoice recognition. OCR, validation logic, and calculation modules were integrated with the client’s existing systems, followed by the implementation of exception handling workflows and an intuitive user interface for accounting staff review. Subsequent pilot and rollout phases focused on model fine-tuning, feedback-driven optimization, and full-scale deployment across all invoice types.
Launched a pilot with a subset of invoices in the preproduction environment.
Fine-tuned extraction models for handwritten and scanned tickets.
Optimized calculation logic for detention time validation.
Expanded automation across all invoice types. Incorporated user feedback loops for correction and learning.
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BENEFITS
Making An Impact On Client Success
The OCR Invoice Automation with an AI-powered ticket time extraction system delivered measurable improvements across efficiency, accuracy, and operational performance. By automating core processes and reducing manual dependency, the system enhanced data reliability. In addition, it accelerated invoicing and empowered teams with actionable insights for continuous optimization.
Automation Efficiency: Increased autoinvoicing from 40% to 70%, reducing manual interventions.
Accuracy Gains: Significant reduction in errors through advanced OCR and calculation validation
Faster Invoicing Cycles: Reduced processing delays, accelerating invoice completion and payment timelines.
Data-Driven Insights: Reports provided visibility into error patterns, enabling continuous process improvement.
Faster invoicing
Enhanced accuracy
Intelligent decisions
Reduced operational costs
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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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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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AI-Integrated Solution for Private Jet Charter Eliminates Empty-Legs & Optimizes Efficiency
Challenges:
Off-the-shelf software limitations, hindered integrations and automation, increased empty-leg flights
Industry
Private Jet
Solutions:
A custom AI solution for precise scheduling & crew management
Results:
Flawless flight-planning & eliminated empty-legs
Location:
US
About The Client
Meet our top-tier client, a leading private jet charter company in the US. They specialize in point-to-point floating fleet operations for premium travelers, ensuring flexible and luxurious experiences.
With a strong focus on safety and reliability, their dedicated team delivers exceptional customer service, setting industry standards in the private jet market.
Case Overview
The company's dependency on off-the-shelf software was a barrier to integrations and operational efficiency, leading to potential risks like 'Empty Leg' scenarios. Realizing the pressing need for modernization, they joined forces with Fingent to seek an integrated solution.
Fingent's personalized approach tackled these challenges, facilitating swift actions and propelling them to the forefront of innovation.
CHALLENGES
Navigating the Skies
Competitive Business Landscape
The private jet charter industry is increasingly competitive, posing challenges for our clients.
Flawless Customer Experience
Provide experiences based on effective flight planning, maintenance, and sales management.
Off-the-Shelf Software Challenges
Dependency on rigid software limits integrations, transparency, and automation.
Risk of 'Empty Leg' Flights
Inadequate systems hinder prompt decision-making, increasing the risk of 'Empty Leg' scenarios.
Immediate Need for Modernization
Recognizing the need, the company sought modern, integrated solutions to maintain competitiveness.
SOLUTION
Fingent’s Innovative Solutions Transforming Private Jet Operations
01Email Monitoring with AI
02AI-based Pricing
03Custom Application for Aircraft Maintenance
04Foreflight Integration
05Flight Risk Assessment Tool
01Email Monitoring with AI
Email Monitoring with AI
Our customized solution with improved integration capabilities helped the client manage and improve sales seamlessly using both Avinode and Airmail. Leveraging AI, we automated email monitoring to identify lucrative opportunities such as trips for the next 30 days, matching fleet types, and relevant airports.
02AI-based Pricing
AI-based Pricing
Fingent implemented AI-based pricing modules for the client, automating price adjustments based on demand. This streamlined sales quoting processes for private jet charter operators, ensuring timely and effective pricing strategies.
03Custom Application for Aircraft Maintenance
Custom Application for Aircraft Maintenance
Fingent developed a tailored application, enabling easy access to vital aircraft maintenance data such as VOR check results and scheduled maintenance. With enhanced insights, the team operates smarter, planning trips with greater efficiency.
04Foreflight Integration
Foreflight Integration
Fingent facilitated seamless integration with ForeFlight, simplifying the process for the client team. By sharing crew and passenger details, weight, and baggage information, they obtain accurate calculations and performance analysis without additional logins or third-party tools.
05Flight Risk Assessment Tool
Flight Risk Assessment Tool
Our application features FRAT, a Flight Risk Assessment Tool ensuring safety measures before each trip. The operations manager or crew completes the assessment, releasing the trip only upon confirmation and enhancing safety protocols.
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BENEFITS
Making an Impact on Client Success
Fingent's customized integrated solution empowers the company to harness data from over 10-15 third-party tools, optimizing trip planning and enhancing the management of crews, sales, and operations. With a streamlined process, our client can now take timely, precise actions to circumvent 'Empty Leg' scenarios and deliver unparalleled customer experiences, staying ahead of the competitive curve.
Efficiently track and manage sales activities, ensuring a seamless booking process.
Streamlined processes boost productivity and reduce operational overheads.
Utilize advanced tools for precise and efficient flight scheduling, minimizing delays.
Assess and mitigate potential risks effectively, ensuring safe and secure journeys.
Integrate various systems and tools for centralized data management and enhanced collaboration.
Accurate Flight Scheduling & Logistics
AI Powered Enhancement
Safe & Secure Travel Experience
Smart trip Planning
Single Integrated Platform
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