Gallup scales real-time coaching for thousands with Amazon Bedrock

How Gallup turned 90 years of workplace science into an AI assistant that gives leaders personalized guidance in seconds, powered by Amazon Bedrock.

Gallup delivers analytics and advice to help leaders and organizations solve their most pressing problems. With more than 90 years of experience and a global reach, Gallup has developed a uniquely deep understanding of workplace behavior and performance.

However, this knowledge wasn’t centralized or delivered in context. Leaders had to navigate multiple resources to find relevant insights and then translate them into action without guidance. The lack of real-time, personalized recommendations meant workplace challenges were often handled reactively instead of proactively.

Gallup needed to transform decades of proprietary research into real-time, personalized guidance that leaders can access instantly within their existing workflow.

In this post, we show how Gallup built Gallup AI, a generative AI assistant powered by Amazon Bedrock. It transforms decades of proprietary workplace research into real-time, personalized coaching delivered directly within the Gallup Access application.

Why Amazon Bedrock

Gallup evaluated multiple approaches to building a generative AI assistant. The team chose Amazon Bedrock for three reasons:

1. Access to leading foundation models like Anthropic’s Claude without managing infrastructure.
2. Built-in retrieval augmented generation (RAG) through Amazon Bedrock Knowledge Bases, a fully managed RAG capability, grounds responses in verified research.
3. Native guardrails to enforce content safety at scale.

This combination allowed Gallup to move from prototype to production in weeks rather than months, without hiring a dedicated machine learning (ML) operations team.

Note: Anthropic’s Claude models on Amazon Bedrock are available in select AWS Regions. For current model and Region availability, see Supported models by Region in Amazon Bedrock.

The approach: Building intelligence into daily workflow

Gallup built Gallup AI, a generative AI-powered assistant integrated directly into Gallup Access. The unified application lets managers review engagement results, build action plans, explore CliftonStrengths insights, and access curated content to better support their teams.

The solution uses Amazon Bedrock with Anthropic’s Claude models to deliver conversational insights grounded in Gallup’s proprietary research. Amazon Bedrock Knowledge Bases and Amazon Kendra retrieve relevant research and organizational data. This is designed to ground responses in verified workplace science. Amazon Bedrock Guardrails enforce content safety policies, while AWS Lambda with FastAPI delivers real-time streaming responses that feel natural and immediate.

The architecture follows a serverless design and supports multiple organizations simultaneously. Amazon ElastiCache Serverless provides sub-millisecond response times for conversation history. Amazon Relational Database Service (Amazon RDS) for MySQL serves as the durable system of record. Amazon Data Firehose streams usage metrics to Amazon Simple Storage Service (Amazon S3) for cost management and performance optimization.

How the solution works

The following diagram shows the Gallup Access AI application architecture.

Figure 1: Gallup Access AI application architecture

Gallup’s proprietary workplace research covers decades of employee engagement studies, performance data, and organizational insights. The content is stored in Amazon S3 and ingested into Amazon Bedrock Knowledge Bases. The application also continuously crawls the Gallup website to capture the latest research publications, articles, and insights, indexing this content in Amazon Kendra for instant retrieval. This dual approach gives the AI assistant access to both historical research archives and current workplace science, delivering responses grounded in verified, up-to-date knowledge rather than generic advice.

When a leader asks Gallup AI a question, the system retrieves relevant research from both Amazon Bedrock Knowledge Bases and Amazon Kendra. The system scores documents based on confidence thresholds, filters them, and consolidates them before sending them to Claude models in Amazon Bedrock.

The conversation flows through AWS Lambda handlers that manage both real-time streaming (for web clients) and synchronous requests (for backend services). Amazon ElastiCache Serverless caches recent conversation history for instant retrieval, while Amazon RDS for MySQL serves as the durable storage layer with organized records of conversations, prompts, responses, and source citations.

Amazon Bedrock Guardrails apply content safety policies during generation, with the ability to intervene mid-stream if policy violations are detected. Interactions persist before streaming begins, preserving transactional integrity even if connections are interrupted.

AWS Systems Manager Parameter Store serves as the application’s centralized configuration hub, managing AI model settings, content safety policies, and performance thresholds. This allows the team to adjust application behavior instantly, without redeploying code or interrupting service for users.

Amazon DynamoDB provides fast, flexible storage for product-specific insights and contextual data, so the application delivers personalized experiences tailored to each user’s role and workflow.

Comprehensive metrics, including I/O tokens, cached tokens, time-to-first byte, and stop reasons, flow through Amazon Data Firehose to Amazon S3, providing visibility into cost, performance, and usage patterns across the application.

What Gallup has achieved

Gallup has transformed decades of workplace research into an intelligent assistant that delivers measurable value across thousands of organizations. Tasks that previously required navigating reports, articles, and tools now resolve through a single conversational interaction. Time to insight dropped from manual research to real-time, AI-delivered guidance within seconds. The application processes billions of tokens through production interactions, with responses grounded in verified workplace science.

Since launching in June 2024, adoption and engagement have grown rapidly:

What the customer said

Gallup’s Director of Product reflects on what this shift means for how leaders access workplace science:

“Gallup AI represents a fundamental shift in how leaders access workplace science. For decades, our research helped organizations make better decisions, but it often required leaders to search, interpret, and apply those insights themselves. By building on Amazon Bedrock, we’re embedding scientifically grounded guidance directly into the flow of work, giving managers real-time support that is both personalized and actionable.”

— Andrew Bridger, Director of Product, Gallup

With this foundation in place, Gallup is focused on expanding what the application can do next.

What’s next

Gallup’s roadmap focuses on making its expertise more accessible, actionable, and embedded into everyday workflows. A key initiative is the development of an AI-curated prompt library that captures the most common questions managers and leaders ask. This library will help users quickly engage with Gallup AI through proven, high-value prompts grounded in workplace research.

In addition, Gallup is introducing guided coaching experiences built around structured conversation flows. These guided prompts walk managers through well-defined coaching scenarios, such as improving engagement, addressing team challenges, or developing employees, by sequencing prompts and responses into purposeful, outcome-driven interactions.

Gallup is building an agent-based foundation using Amazon Bedrock AgentCore. This positions Gallup AI to move beyond a user-facing assistant. By surfacing tools, workflows, and proprietary knowledge programmatically, the system can support not only end users but also other systems and integrations across the application.

Conclusion

By combining the generative AI capabilities of Amazon Bedrock with Gallup’s proprietary workplace research, leaders now have instant access to scientifically grounded guidance exactly when they need it. The serverless architecture enables the application to scale reliably while delivering low-latency streaming responses and comprehensive observability.

To build your own generative AI application, get started with Amazon Bedrock. To learn more about grounding responses in your own data, explore Amazon Bedrock Knowledge Bases.

Further reading

• AWS prescriptive guidance: Writing best practices for RAG applications : Optimize your knowledge sources for better AI retrieval and response quality.
• Build safe Generative AI applications like a Pro: Best practices with Amazon Bedrock Guardrails : Configure and deploy AI safety guardrails in production.
• Well-Architected Framework, Generative AI Lens : Apply proven best practices for generative AI workloads on AWS.

About the authors