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.
