Picture this: You’re a financial analyst starting your Monday morning with a steaming cup of coffee, ready to review your investment portfolio. But instead of manually scouring dozens of news websites, financial reports, and industry analyses, you simply ask your AI assistant: “What global events happened over the weekend that might impact my technology stock holdings?” Within seconds, you receive a comprehensive analysis of relevant news, sentiment scores, and potential investment implications—all powered by a sophisticated generative AI application you built yourself.
This scenario isn’t science fiction; it’s the reality that modern financial professionals can create today. In an era where information moves at the speed of light and industry conditions can shift dramatically overnight, staying informed isn’t just an advantage—it’s essential for survival in competitive financial landscapes. The challenge lies in processing the overwhelming volume of global information that could impact investments while distinguishing reliable insights from noise.
Amazon SageMaker – Develop and Scale AI Use Cases with the Broadest Set of Tools
Luckily for us, technology is making this more straightforward. The next generation of Amazon SageMaker with Amazon SageMaker Unified Studio is a single data and AI development environment where you can find and access the data in your organization and act on it using the best tools across different use cases. SageMaker Unified Studio brings together the functionality and tools from existing AWS analytics and artificial intelligence and machine learning (AI/ML) services, including Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock, and Amazon SageMaker AI. From within SageMaker Unified Studio, you can find, access, and query data and AI assets across your organization, then work together in projects to securely build and share analytics and AI artifacts, including data, models, and generative AI applications.
With SageMaker Unified Studio, you can efficiently build generative AI applications in a trusted and secure environment using Amazon Bedrock. You can choose from a selection of high-performing foundation models (FMs) and advanced customization capabilities like Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, Amazon Bedrock Agents, and Amazon Bedrock Flows. You can rapidly tailor and deploy generative AI applications and share with the built-in catalog for discovery.
What makes SageMaker Unified Studio particularly powerful for organizations is its integration with Amazon Bedrock Flows to build generative AI workflows, which is changing how organizations think about AI application development.
Amazon Bedrock Flows for Generative AI Application Development
With Amazon Bedrock Flows, you can build and execute complex generative AI workflows without writing code, using an intuitive visual interface that democratizes AI development. This capability is transformative for organizations where speed, accuracy, and adaptability are paramount. It offers the following benefits:
- Visual workflow development – Users can design AI applications by dragging and dropping components onto a canvas, making AI logic transparent and modifiable.
- Business logic flexibility – The service supports complex business logic through conditional branching, multi-path decision trees, and dynamic routing.
- Democratizing AI development – Business experts can directly contribute to AI application development without requiring extensive technical expertise.
- Seamless integration – Amazon Bedrock Flows integrates with FMs, knowledge bases, guardrails, and other AWS services.
- Reduced development complexity – The service handles infrastructure management and scaling through serverless execution and SDK APIs.
Solution Overview
In this post, we explore a financial use case, in which we want to stay on top of the latest global events and determine our investment or financial exposure based on this. We can use a SageMaker Unified Studio flow application to pull in the latest news summaries, derive sentiment based on news summary, and determine their effects on investments. The following diagram illustrates this use case.
In the following sections, we show how to create a new project and build a flow application using a generative AI profile in SageMaker Unified Studio.
Prerequisites
For this walkthrough, you must have the following prerequisites:
- A demo project – Create a demo project in your SageMaker Unified Studio domain. For instructions, see Create a project. For this example, we choose All capabilities in the project profile section, which includes the generative AI project profile enabled.
Create New Project and Build a Flow Application in SageMaker Unified Studio
In this section, we create a new flow application that uses an Amazon Bedrock knowledge base to provide information about the user’s investment portfolio. Complete the following steps:
- In SageMaker Unified Studio, open the project you created as a prerequisite and choose Build and then Flow.
- Drag Knowledge Base from Nodes to the design panel to add a knowledge base that will include the user’s investment portfolio and news articles and other information like earnings call transcripts, financial analyst reports, and so on.
- Choose the Knowledge Base node and configure it as follows:
- Add a name for your knowledge base (for example,
portfolio…). - Choose the model (for example, Claude 3.5 Haiku).
- Choose Create new Knowledge Base.
- Enter a name for the knowledge base.
- Select Project data source.
- For Select a data source, choose the Amazon Simple Storage Service (Amazon S3) bucket location where you uploaded your data.
- Choose Create.
The knowledge base creation process takes a few minutes to complete.
- When the knowledge base is ready, choose Save to save it to the flow.
- Choose My components, and on the options menu (three vertical dots), choose Sync to sync the knowledge base.
Make sure the S3 bucket has all the data (user portfolio data and latest news information data) before syncing the knowledge base.
We don’t provide any financial or news information data as part of this post. Upload current events or news data and investment portfolio data from your own data sources.
Test the Flow Application
After the knowledge base sync is complete, you can return to the flow application and ask questions. Using SageMaker Unified Studio flows, a financial analyst can provide a more personalized and customized financial outlook to their customers using rich internal financial information on their customer’s investment portfolio and the latest publicly available current events and news information. The following are some example questions that you can ask to test the knowledge base:
Check if Tesla or Apple is in any of the user's investment portfolio
Flow-based applications offer a visual approach to creating complex AI workflows. By chaining different nodes, each optimized for specific functions, you can create sophisticated solutions that are more reliable, maintainable, and efficient than single-prompt approaches. These flows allow for conditional logic and branching paths, mimicking human decision-making processes and enabling more nuanced responses based on context and intermediate results.
Clean Up
To avoid ongoing charges in your AWS account, delete the resources you created during this tutorial:
- Delete the project.
- Delete the domain created as part of the prerequisites.
Conclusion
In this post, we demonstrated how to use Amazon Bedrock Flows in SageMaker Unified Studio to build a sophisticated generative AI application for financial analysis and investment decision-making without extensive coding knowledge. With this integration, you can create sophisticated financial analysis workflows through an intuitive visual interface, where you can process industry data, analyze news sentiment, and assess investment implications in real time. The solution integrates seamlessly with AWS services and FMs while providing essential features like automatic scaling, compliance controls, and audit capabilities. The implementation process involves setting up a SageMaker Unified Studio domain, configuring knowledge bases with portfolio and news data, and creating visual workflows that can analyze complex financial information. This democratized approach to AI development allows both technical and business teams to collaborate effectively, significantly reducing development time while maintaining the sophisticated capabilities needed for modern financial analysis.
To get started, explore the SageMaker Unified Studio documentation, set up a project in your AWS environment, and discover how this solution can transform your organization’s data analytics capabilities.
About the Authors
Amit Maindola is a Senior Data Architect focused on data engineering, analytics, and AI/ML at Amazon Web Services. He helps customers in their digital transformation journey and enables them to build highly scalable, robust, and secure cloud-based analytical solutions on AWS to gain timely insights and make critical business decisions.
Arghya Banerjee is a Sr. Solutions Architect at AWS in the San Francisco Bay Area, focused on helping customers adopt and use the AWS Cloud. He is focused on big data, data lakes, streaming and batch analytics services, and generative AI technologies.
Melody Yang is a Principal Analytics Architect for Amazon EMR at AWS. She is an experienced analytics leader working with AWS customers to provide best practice guidance and technical advice to assist their success in data transformation. Her areas of interest are open-source frameworks and automation, data engineering, and DataOps.
Gaurav Parekh is a Solutions Architect at AWS, specializing in generative AI and data analytics, with extensive experience building production AI systems on AWS.