This is a guest post co-authored by Evan Miller, Noah Kershaw and Valerie Renda of Kepler Group
Kepler, a global, full-service digital marketing agency serving Fortune 500 brands, understands the delicate balance between creative marketing strategies and data-driven accuracy. Our company name echoes our commitment to drawing inspiration from the fantastic astronomer Johannes Kepler, bringing clarity to complex challenges and revealing the progress of our clients.
In this post, we share how Amazon Q Business implementation transformed operations by maintaining strict security standards by democratizing AI access across the organization, resulting in an average saving of 2.7 hours per employee per week in manual work and improved client service delivery.
Challenge: Balance between innovation and security
As a digital marketing agency working with Fortune 500 clients, we have put pressure on AI capabilities to maintain the highest level of data security while using them. Previous solutions lacked critical features, which led team members to consider more general solutions. Specifically, the original implementation lacked important features such as the chat history feature, preventing users from accessing or referring to previous conversations. Due to the lack of conversational context, users had to repeatedly provide background information for each interaction. Furthermore, the solution did not have file upload capabilities, restricting users to text-only interactions. These limitations provided a basic AI experience that users had to compromise on by rewriting prompts, manually maintaining context and avoiding the inability to handle various file formats. The limited capabilities ultimately led the team to explore alternative solutions that could better meet their comprehensive needs. As an International Organization for Standardization (ISO) 27001 certified organization, we needed an enterprise-grade solution that met strict security requirements without compromising on functionality. Our ISO 27001 certification requires strict security controls, so public AI tools were not suitable for our needs. We needed a solution that could be implemented in a secure environment while maintaining full compliance with strict security protocols.
Why did you choose Amazon Q Business?
Our decision to implement Amazon Q business was driven by three key factors that perfectly match our needs. First, the integration process was seamless as Kepler Intelligence Platform (KIP) infrastructure was already living in Amazon Web Services (AWS). The Amazon Q Business implementation uses three core connectors: Amazon Simple Storage Service (Amazon S3), Google Drive, and Amazon Athena), but the broad data ecosystem includes 35-45 different platform integrations, primarily flowing through Amazon S3. Second, our commitment from Amazon Q Business to not use data for model training met our key security requirements. Finally, the Amazon Q Business Apps feature allowed us to develop no-code solutions to everyday challenges to democratize access to efficient workflows without the need for additional software developers.
The journey of implementation
We began our journey to implement Amazon Q Business in early 2025, launching a focused pilot group with 10 participants, expanding to 100 users in February and March, and planning a full deployment to over 500 employees. During this period, we organized an AI-centric hackathon that catalyzed organic adoption and sparked creative solutions. This implementation is unique in the way that Amazon Q Business is integrated into your existing Kepler Intelligence platform and rebrand it kip ai Maintain consistency with internal systems.
KIP AI demonstrates how AI capabilities are comprehensively integrated with existing data infrastructure. I use multiple data sources for my storage needs, including Amazon S3, Amazon Quicksight for Business Intelligence requirements, and Google Drive for team collaboration. At the heart of our system is custom extraction, transformation and load ETL pipelines (KIP SSOT). We have configured Amazon Q Business to seamlessly connect with these data sources, allowing team members to access insights through both the web interface and browser extensions. The following diagram illustrates the architecture of KIP AI.
This integrated approach helps Kepler employees securely access AI capabilities while maintaining data governance and security requirements that are critical to their clients. Access to the platform is protected through AWS Identity and Access Management (IAM), connects to a single sign-on provider, making the system available only to certified personnel. This careful approach to security and access management was important in deploying AI capabilities across the organization while maintaining client trust.
Transformative Use Cases and Results
The implementation of Amazon Q Business has revolutionized several key areas of our operations. The requesting information (RFI) response process, which traditionally consumed considerable time and resources, has been dramatically streamlined. The team currently reports that they can save more than 10 hours per RFI response, allowing them to efficiently pursue more business opportunities.
There were also significant improvements in client communication. The platform helps you draft clear, consistent, and timely communications, from regular emails to comprehensive status reports and presentations. This improved quality of communication has strengthened client relationships and improved service delivery.
Perhaps most importantly, we have achieved incredible efficiency gains across the organization. Our employees report an average of 2.7 hours per week on manual work, with user satisfaction rates exceeding 87%. This platform has enabled us to standardize our approach to insight generation and ensure consistent, high quality service delivery across all client accounts.
Looking ahead
As we expand Amazon Q's business access to all Kepler employees (over 500) over the next few months, we maintain a thoughtful approach to deployment. We recognize that some clients have specific requirements for AI use, and carefully balance innovation with client preferences. This strategic approach involves working to renew client contracts and helping clients feel more comfortable with AI integration while respecting current guidelines.
Conclusion
Our experience with Amazon Q Business demonstrates how enterprise-grade AI can be successfully implemented while maintaining strict security standards and respecting client preferences. The platform not only improved our operational efficiency, but also improved our ability to provide consistent, high quality services to our clients. What's particularly impressive is the rapid deployment of the platform. The solution could be implemented within a few weeks without coding requirements, eliminating the ongoing model maintenance and data source control costs. As we continue to expand our use of Amazon Q Business, we are excited about the possibilities for further innovation and efficiency improvements in our digital marketing services.
About the author
Evan MillerGlobal Head of Products and Data Science is a strategic product leader who participated in Kepler 2013. He currently serves as global head of product and data science and owns the end-to-end product strategy for Kepler Intelligence Platform (KIP). Under his leadership, KIP has gained industry recognition, has won awards for Best Performance Management Solutions and Best Commerce Technology, and has driven critical business impact through innovative features such as automated machine learning analytics and marketing mix modeling technology.
Noah Kershaw He leads the product team at Kepler Group, a global digital marketing agency that helps brands connect with their audiences through data-driven strategies. Passionate about innovation, Noah is at the forefront of integrating AI solutions to enhance client services and streamlining operations. His collaborative approach and enthusiasm for leveraging technology was important in bringing Kepler's “Future in Focus” vision into life.
Valerie Renda, Director of Data Strategy and Analytics; It specializes in data strategies, analytics, and marketing systems strategies within digital marketing. This is an area she has worked for over eight years. At Kepler, she has made a significant contribution to data management and Martech strategies for various clients. She has contributed to major data infrastructure projects, including implementing customer data platforms, implementing business intelligence visualizations, server-side tracking, maltech integration, and tag migration. She is also leading the development of workflow tools to automate data processes, streamline advertising operations and improve internal organizational processes.
Al destefano I am a Sr. Generated AI Specialist on the Amazon Q GTM team based in New York City. AWS uses technical knowledge and business experience to convey the benefits of tangible companies when using managed-generated AI AWS services.
Sunanda Patel He is a senior account manager with over 15 years of expertise in the management consulting and IT sector, with a focus on business development and talent management. Throughout her career, Sunanda has managed a diverse range of client relationships, ranging from nonprofits to businesses and large multinational companies. Sunanda joined AWS in 2022 as the Account Manager for Manhattan's Commercial Division and is currently working with strategic commercial accounts to help grow on the cloud journey to achieve complex business goals.
Kumar Kara This is AWS' Sr. Solutions Architect that supports SMBS. He is an experienced engineer with deep experience in the software development lifecycle. Kumar is trying to solve difficult problems by applying technical, leadership and business skills. He holds a Masters degree in Computer Science and Machine Learning from Georgia Institute of Technology and is based in New York (USA).
