Skip to main content

AWS Solutions Library

Guidance for Investment Analysis Using Amazon Bedrock

Overview

This Guidance demonstrates how Amazon Bedrock, which offers a range of large language models (LLMs), can perform generative AI-powered analysis on structured and unstructured data sets to support investment analysts. Tools offered by AWS generative AI services process complex instructions, such as investment analysis and goals. The resulting analysis is presented as a text summary, referencing relevant data to support the reasoning, enabling investment analysts to actively manage investments for institutional or individual clients more effectively.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Get Started

Deploy this Guidance

Sample code

Use sample code to deploy this Guidance in your AWS account

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Amazon Bedrock and Lambda enable your application to scale automatically based on demand, eliminating the need for manual infrastructure management. These services ensure your application can handle fluctuating user demand with ease, providing high availability and fault tolerance through managed services.

Read the Operational Excellence whitepaper 

Safeguard your application and user data with Amazon Cognito , which provides secure user authentication and authorization. Secrets Manager securely stores sensitive credentials, preventing exposure in your application's code or configuration. Enhance your website's security with CloudFront , which offers traffic encryption and access controls. Use AWS Identity and Access Management (IAM) policies to scope down to the minimum permissions required, limiting unauthorized access to resources.  

Read the Security whitepaper 

Elastic Load Balancing (ELB) routes traffic requests from the store’s mobile application to healthy Amazon Elastic Compute Cloud (Amazon EC2) instances. Distribute your Streamlit-based frontend globally with CloudFront , caching content closer to your users for improved reliability and availability. By incorporating a monitoring and observability service services like Amazon CloudWatch , you can quickly identify and resolve reliability issues. The synchronous loose coupling provided by ELB reduces the chance of application failure, so your users can browse the mobile application without encountering downtime errors.

Read the Reliability whitepaper 

Lambda and Amazon Bedrock Agents handle high-volume traffic, provide low-latency responses, and scale automatically to meet your application's evolving performance needs. Additionally, CloudFront reduces latency for your users by caching content closer to them, improving the perceived performance of your application.

Read the Performance Efficiency whitepaper 

Lambda functions are charged based on the number of invocations and the duration of execution, allowing your application to run without incurring fixed infrastructure costs. With Amazon Bedrock , you pay only for what you consume through input and output token pricing, without the need to manage or handle the underlying infrastructure. By using these serverless and managed services, your application can scale up and down as needed, paying only for the resources it consumes, and minimizing the overall operational costs.

Read the Cost Optimization whitepaper 

Databse instances powered by AWS Graviton3 processors enable you to reach your sustainability innovation goals faster and with 60 percent less energy consumption than comparable Intel-based processors.

Read the Sustainability whitepaper 

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.