For a leading wealth management and financial services organization, delivering exceptional customer service depends on providing accurate information quickly and consistently. Supporting customers across banking, investment management and wealth advisory services, the organization manages a significant volume of interactions every day through service centers, advisory meetings, phone conversations and digital channels.
Every customer interaction presents an opportunity to strengthen trust. It also creates an expectation that questions will be answered accurately and without delay.
To support those interactions, customer-facing representatives relied on a broad ecosystem of internal knowledge assets, including product documentation, service procedures, operational guidelines, policy information and customer support content. These resources contained the information employees needed to answer questions, resolve issues and guide customers through a variety of financial services processes.
The challenge was not a lack of knowledge. The challenge was accessing and applying that knowledge efficiently during live customer interactions.
When customers reached out with questions, representatives typically searched internal SharePoint repositories to locate relevant information. Search results often returned multiple documents and articles, requiring employees to review content, identify the information most relevant to the inquiry and manually synthesize a response before communicating with the customer.
The process depended heavily on the experience of the representative. More experienced employees could often locate and interpret information faster, while newer team members required additional time to navigate documentation and validate responses. Depending on the complexity of the inquiry, the process could take anywhere from two to ten minutes before an answer was ready to be shared.
While a few minutes may appear insignificant in isolation, the impact became substantial at enterprise scale. Thousands of representatives were supporting customers every day and every interaction required time spent searching for information rather than serving customers directly. The result was a hidden productivity challenge that affected both employee efficiency and customer experience.
The friction showed up in four ways:
- Valuable employee time was consumed by searching, reviewing and interpreting documentation
- Customer response times varied depending on employee experience and subject-matter familiarity
- Customers often waited during calls, meetings or email exchanges while representatives researched answers
- Service organizations faced increasing pressure to improve productivity without compromising service quality
As customer expectations continued to rise, the organization recognized an opportunity to modernize how knowledge was accessed and delivered. The question was no longer whether the information existed. The question was how to transform enterprise knowledge into customer-ready answers in seconds rather than minutes.
Building An AI-Powered Knowledge Assistant
Persistent addressed the challenge by designing and implementing a GenAI-powered knowledge assistant built on a Retrieval-Augmented Generation (RAG) architecture. The goal was not to replace customer-facing employees or automate customer interactions. It was to reduce the effort required to locate information and accelerate the delivery of accurate, customer-ready responses.
The engagement focused on transforming existing knowledge assets into an intelligent knowledge experience capable of supporting thousands of customer service and advisory interactions every day. Rather than requiring representatives to manually search multiple documents and interpret findings on their own, the new solution enabled employees to ask questions in natural language and receive summarized answers grounded in approved enterprise content.
Persistent worked closely with client SMEs and architects while providing end-to-end engineering ownership across the solution lifecycle. The engagement included platform engineering, application development, prompt engineering, knowledge curation pipelines, monitoring and operational support. The result was a production-ready GenAI solution designed to scale alongside the organization’s customer service operations.
The engagement moved forward through four design choices that mattered:
- Enterprise knowledge was centralized into a searchable intelligence layer capable of supporting natural language queries
- Retrieval-Augmented Generation was used to ground responses in approved internal content rather than relying solely on model-generated outputs
- Human review remained part of the workflow to ensure customer-facing responses met organizational standards
- Continuous monitoring, optimization and engineering support ensured the solution could evolve alongside changing business needs
As enterprises move from AI experimentation to production deployment, successful initiatives increasingly focus on augmenting employee productivity rather than replacing human expertise. This engagement followed the same principle by combining AI-driven knowledge retrieval with human judgment and oversight.
The value was not in automating customer service. It was in making customer service representatives dramatically more efficient.
Where Enterprise Knowledge Turns Into Customer-Ready Answers
At the core of the solution was an AI-powered workflow designed to reduce the time required to locate, interpret and communicate information. Instead of forcing employees to manually search through large volumes of documentation, the platform transformed enterprise knowledge into a searchable intelligence layer capable of generating contextual responses in real time.
What had previously been a multi-step research process became a streamlined workflow that combined knowledge retrieval, contextual understanding and AI-powered response generation.
The workflow operated through four coordinated stages:
- Knowledge ingestion and indexing: Internal articles, procedures, policies and support documentation were ingested into a searchable repository designed for AI-driven retrieval
- Contextual search and retrieval: When a representative submitted a question, the platform identified the most relevant information from approved enterprise knowledge sources
- AI-powered response generation: Retrieved content was passed to a large language model, which generated a concise, customer-friendly summary tailored to the inquiry
- Human validation and delivery: Representatives reviewed the response before sharing it with customers, ensuring accuracy and maintaining appropriate oversight for customer-facing communications
The result was a workflow that balanced speed with trust. Representatives no longer needed to spend several minutes searching through documentation to assemble answers manually. Instead, they could focus their attention on serving customers while leveraging AI to accelerate knowledge discovery.
Together, these capabilities transformed knowledge retrieval from a manual research exercise into a scalable AI-assisted service experience. What once took minutes could now be accomplished in seconds, helping customer-facing teams spend less time searching and more time engaging customers.
From Document Search to Customer-Ready Answers
The GenAI-powered knowledge assistant delivered measurable improvements across customer service operations by reducing the time required to locate information and respond to customer inquiries. What had previously been a manual, document-intensive process became a faster and more streamlined experience for customer-facing teams.
The engagement delivered measurable gains across productivity, efficiency and customer support operations:
- Approximately two minutes saved per customer interaction through AI-assisted knowledge retrieval and response generation
- More than 18,000 users supported through the initial production deployment
- Increased service capacity by enabling representatives to spend less time searching for information and more time supporting customers
- Improved response speed across thousands of daily customer interactions
- Greater consistency in how information was surfaced and communicated to customers
- Reduced effort associated with navigating multiple knowledge repositories and support documents
The impact became particularly significant when viewed at enterprise scale. With more than 18,000 users leveraging the platform and thousands of customer interactions taking place each day, even modest time savings translated into substantial productivity gains across the organization.
For the client, the visible result was faster access to information. The strategic result was improved operational efficiency across customer-facing teams without sacrificing quality or accuracy.
Every minute removed from knowledge discovery became another minute available for customer engagement.
Extending the Model Beyond Knowledge Retrieval
The success of the initial deployment established a foundation for a broader GenAI transformation program across the organization. What began as a knowledge-assistance use case evolved into a longer-term engagement focused on applying AI to additional customer service and productivity workflows.
The engagement itself expanded over multiple years, with production deployments already supporting multiple use cases and additional initiatives underway.
The foundation now supports a broader agenda:
- Expanding AI-powered assistance into additional customer-facing workflows
- Scaling GenAI capabilities across multiple business functions and service operations
- Creating more intelligent and context-aware employee experiences
- Reducing operational effort associated with knowledge discovery and information retrieval
- Establishing a reusable framework for future enterprise AI initiatives
As enterprises continue moving from experimentation to scaled AI adoption, the greatest value increasingly comes from embedding AI directly into everyday workflows. This engagement demonstrated how targeted GenAI applications can create measurable operational value when applied to high-volume processes that affect both employee productivity and customer experience.
The opportunity is no longer limited to answering questions faster. It is about reimagining how knowledge is accessed and applied across the enterprise.
Why Persistent Was Chosen
Persistent was selected to help transform a high-volume, knowledge-intensive workflow into a scalable GenAI-powered experience. The engagement required more than model implementation. It demanded a combination of engineering expertise, enterprise-scale delivery capabilities and sustained partnership across multiple phases of deployment.
Persistent stood out for reasons that were both technical and operational:
- End-to-end engineering ownership across application development, platform engineering, data pipelines, monitoring and ongoing optimization
- Deep expertise in GenAI application development and Retrieval-Augmented Generation architectures
- Strong collaboration with client SMEs and architects to align technical implementation with business needs
- Continuous leadership engagement and governance throughout the lifecycle of the program
- Proven ability to scale, operate and evolve enterprise AI solutions over a multi-year engagement
The engagement demonstrated how GenAI can create meaningful business value when paired with strong engineering discipline, operational rigor and a deep understanding of enterprise workflows. More than a successful AI implementation, it established a scalable foundation for future innovation while helping customer-facing teams spend less time searching and more time serving.
For the client, the outcome was not simply faster answers. It was a more efficient, scalable and intelligent approach to customer service.
