What was the impact of the AI solution?

Significantly boosted analyst productivity by automating complex data synthesis workflows. Enabled faster, more accurate investment decisions through integrated multi-modal AI analysis. Provided serverless, scalable architecture with conversation memory and modular tool orchestration, simplifying AI assistant development and maintenance.

Rank#1,467of 3,449

Source

Evidence: Low10/100

AI-powered Investment Research Assistant Using Amazon Bedrock Agents

Use case typeInvestment researchUpdated Jun 13, 2026

AWS developed an AI-powered assistant for financial analysts using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and other AWS AI services to synthesize multi-modal financial data including reports, earnings calls, and stock data. Analysts face challenges managing diverse data sources and tools under time pressure; the assistant automates querying, analysis, and insight generation by orchestrating foundation models, APIs, and knowledge bases. The assistant uses Retrieval Augmented Generation (RAG) architecture to provide context-aware, accurate responses to user prompts, integrating various tools for financial phrase detection, sentiment analysis, portfolio optimization, and stock queries. Serverless deployment, conversation memory, and traceability improve usability and analyst productivity for timely investment decision-making.

Industry: Finance
Location: United States
Published: June 2024

Reported outcomes

Strategic outcomes

Speed & agilityAutomated complex data synthesis workflowsBetter decisions & insightEnabled faster, more accurate investment decisionsScale & capacityProvided serverless, scalable architecture

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Not established
Provider: AWS
Maturity: Unknown
Linked source: AWS Machine Learning Blog

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1AI Assistants
2Investment Research
3Multi-modal Data Analysis

Financial analysts must rapidly synthesize large, diverse data sets from unstructured and structured sources to generate accurate investment insights under time constraints.

Built an AI assistant using Amazon Bedrock Agents to orchestrate multi-modal data analysis, leveraging foundation models and action groups for modular tool integration.
Utilized Amazon Bedrock Knowledge Bases to provide context retrieval from vectorized document storage and databases for retrieval augmented generation.
Implemented a multi-step orchestration process that breaks down user prompts, plans tasks, and executes using multiple AWS AI/ML services including Amazon Comprehend and Amazon Athena.

Technologies

Amazon Bedrock Amazon Bedrock Agents Amazon Bedrock Knowledge Bases Amazon Comprehend Amazon OpenSearch Serverless Amazon S3 Amazon Athena

Significantly boosted analyst productivity by automating complex data synthesis workflows.
Enabled faster, more accurate investment decisions through integrated multi-modal AI analysis.
Provided serverless, scalable architecture with conversation memory and modular tool orchestration, simplifying AI assistant development and maintenance.

Architecture

The assistant uses Amazon Bedrock Agents to orchestrate querying and reasoning over multi-modal financial data (text, audio, tabular) using foundation models. It integrates with Amazon Bedrock Knowledge Bases for RAG by embedding financial reports in OpenSearch Serverless vector databases and querying stock data via Amazon S3 and Athena. Action groups implemented via AWS Lambda allow seamless interaction with NLP tools and data sources to complete specific tasks like sentiment analysis and portfolio optimization.

Sources & evidence1

Evidence: Low10/100Evidence strength