GenAI Knowledge Assistant Supercharging Contact Center Productivity

Faster Answers, Compliant by Design

A leading Midwest credit union serving a large member base needed to modernize its contact center as agent productivity strained against rising member expectations. Myridius built a GenAI Knowledge Assistant on Amazon Q and AWS S3 with embedded safety guardrails and role-based access, delivering instant, accurate, and compliant responses for agents and establishing a reusable GenAI foundation that can scale to additional enterprise functions.

Key Outcomes

Faster decision-making, with agents accessing contextual responses in seconds rather than minutes.
Boosted agent productivity through high-volume query handling and reduced response times.
Compliant AI responses, with safety guardrails and role-based access controls embedded by design.

Overview

A leading Midwest credit union serving a large member base needed to modernize its customer service operations. Contact center agents were navigating manual knowledge searches across disparate sources, which slowed response times, produced inconsistent answers, and eroded member satisfaction. Myridius leveraged Amazon Q and AWS S3 to build a GenAI Knowledge Assistant, establishing foundational capabilities in MVP1 and architecting for enterprise-wide expansion. The solution embeds safety guardrails and role-based access so every AI-generated response is compliant by design, configures AWS S3 knowledge repositories for multi-source search across structured and unstructured data, and optimizes retrieval to minimize hallucinations. The result is faster, more accurate member service with a reusable GenAI foundation that can scale beyond the contact center.

Client Context

The client is a leading Midwest credit union serving a large and growing member base across a multi-state footprint. Its commercial model rests on the cooperative principle that members are the owners, which makes member service the brand. Every interaction shapes how members talk about the credit union to neighbors, employers, and family, and every slow or inconsistent answer compounds against a value proposition built on personal trust and accessible service.

The credit union sector is also in the middle of a sustained shift in member expectations. Members increasingly compare their credit union experience to digital-first banking apps, instant chat services, and consumer-grade GenAI tools they use elsewhere in their lives. A contact center that runs on manual knowledge searches falls behind that expectation curve quickly, and the cost shows up in member satisfaction scores and retention conversations.

The Challenge

Contact center agents were doing the work of the brand promise with tools that did not match it. Every member query meant navigating multiple knowledge sources by hand, comparing what each said, and constructing an answer that might or might not match what another agent on the same shift gave to a different member. Response times stretched, first-call resolution rates suffered, and the inconsistency itself eroded the trust the cooperative model is built on.

The credit union sought a scalable GenAI-powered solution that could equip agents with instant, accurate, and compliant responses, starting with the contact center and expanding to other enterprise functions over time. The requirement was not just a chatbot. It was a governed foundation that would handle a regulated financial services environment with the safety and access controls the work demanded.

STATUS QUO (BEFORE)	DESIRED STATE (AFTER)
Contact center agents relied on manual knowledge searches across disparate information sources.	GenAI Knowledge Assistant on Amazon Q delivering contextual responses against a unified knowledge base.
Slow response times, inconsistent answers, and reduced member satisfaction.	Faster, more accurate, more consistent responses with measurable improvement in first-call resolution.
No scalable GenAI foundation for additional enterprise use cases.	Reusable MVP1 GenAI foundation architected for rapid expansion to operations, lending, and member services.
Concerns about AI compliance, accuracy, and regulatory fit.	Embedded safety guardrails and role-based access ensuring compliant, policy-adherent AI responses.
Disparate knowledge sources across the credit union.	AWS S3-based knowledge repositories with structured and unstructured ingestion and multi-source search.

Transformation Goals

The engagement focused on north stars that connected member service outcomes to productivity, service quality, and a scalable GenAI foundation the credit union could measure inside its existing operational cadence.

Agent Productivity: Dramatically improve contact center agent efficiency by providing instant access to relevant knowledge and contextual responses through a GenAI-powered assistant.
Service Quality: Deliver faster, more accurate, and more consistent responses to member queries, improving first-call resolution rates and overall member satisfaction.
Scalable Foundation: Build foundational GenAI capabilities in MVP1 that can be rapidly expanded to additional enterprise use cases beyond the contact center.

The Solution

Myridius approached the engagement as a governed GenAI foundation rather than a contact center tool deployment. The team deployed Amazon Q as the core engine, configured the AWS S3 knowledge infrastructure for multi-source coverage of member-facing information, conducted extensive accuracy optimization, and embedded the safety and access controls a regulated financial services environment requires. Every architecture decision was tied to either a member service outcome or a compliance control the credit union and its regulators would recognize.

Orchestrated the foundation: Deployed Amazon Q as the core GenAI engine, enabling natural language queries against the credit union's knowledge base for fast, contextually accurate responses. Configured AWS S3-based knowledge repositories with structured and unstructured data ingestion, streamlined data processing pipelines, and multi-source knowledge search for comprehensive coverage of member-facing information.
Embedded intelligence and performance into the workflow: Conducted extensive testing and optimization of knowledge search accuracy, fine-tuning retrieval parameters and response generation to maximize relevance and minimize hallucinations, the accuracy member-facing AI requires to be useful rather than risky.
Reimagined the operating model: Embedded safety guardrails and role-based access control mechanisms ensuring compliant, secure, and policy-adherent AI-generated responses across all contact center interactions. Structured MVP1 as a reusable GenAI foundation designed for rapid expansion across operations, lending, and member services.

Governance and Trust

Because the credit union operates in a regulated financial services environment serving member-owners, governance was treated as a primary architectural input rather than a downstream review. Safety guardrails were embedded directly into the GenAI response pipeline, ensuring that every AI-generated response is shaped by the credit union's policies, regulatory obligations, and member-protection commitments before it reaches an agent or a member. Role-based access controls govern who can ask what, who can see what, and how sensitive information flows through the system.

The knowledge infrastructure on AWS S3 was designed with consistent ingestion patterns and data processing pipelines so the underlying source of truth is auditable and the AI's behavior is defensible. Response accuracy was tested and optimized to minimize hallucinations, the single most important safety control for member-facing GenAI in financial services. The MVP1 foundation deliberately leaves room for expanded governance as the assistant scales to additional enterprise functions, so compliance does not need to be re-litigated each time a new use case is added.

Results

The transformation moved the contact center from a posture where agent capability was bottlenecked by tooling to one where every agent has the equivalent of an instant expert at their side. Outcomes show up across decision speed, productivity, service quality, and compliance posture, with each reinforcing the others.

The result:

Faster decision-making, with contact center agents accessing relevant knowledge and receiving contextual responses in seconds rather than minutes, dramatically accelerating service delivery.
Boosted agent productivity through high-volume query handling and reduced response times, enabling agents to serve more members per shift while maintaining quality.
Improved member service through faster, more accurate issue resolution, with measurable improvements in first-call resolution and member satisfaction, all delivered through compliant AI responses governed by safety guardrails and role-based access. The MVP1 foundation is built to scale into operations, lending, and additional member services.

Before and After

The following shifts show how the engagement moved the organization from manual, reactive, and siloed operations toward embedded, proactive, and unified ways of working.

Operational Area	Before Myridius	After Myridius
Knowledge Access for Agents	Manual searches across disparate information sources with inconsistent answers.	GenAI Knowledge Assistant on Amazon Q delivering contextual responses from a unified knowledge base.
Response Speed	Slow lookup and answer construction stretching call handle times.	Contextual responses in seconds, dramatically accelerating service delivery.
Answer Consistency	Inconsistent answers between agents and shifts.	Consistent, governed responses anchored to a single source of truth.
AI Compliance Posture	Concerns about regulatory fit, accuracy, and safety of any AI deployment.	Safety guardrails and role-based access embedded into the GenAI response pipeline.
Knowledge Infrastructure	Disparate sources with no unified retrieval pattern.	AWS S3-based knowledge repositories with structured and unstructured ingestion and multi-source search.
Foundation for Future GenAI	No reusable GenAI capability to extend across the enterprise.	MVP1 foundation designed for rapid expansion to operations, lending, and additional member services.

Technology Stack

GenAI Engine

Amazon Q
Answers natural language queries against the knowledge base

Knowledge Storage

AWS S3
Stores structured and unstructured knowledge sources

Cloud Platform

AWS
Hosts the assistant and supporting services

Security

Role-based access control, safety guardrails
Ensure compliant, secure, policy-adherent responses

Data Processing

Automated knowledge ingestion and search pipeline
Keeps the knowledge base current and searchable

In a credit union contact center, the speed and consistency of every answer shapes member trust. This case shows how a compliant, well-grounded GenAI assistant turns scattered knowledge into instant, reliable service. This was not a chatbot bolt-on. It was a compliant, scalable GenAI knowledge foundation.