Build a Serverless AI Document Analyzer with Amazon Bedrock and S3
Learn how to build a simple serverless document-analysis workflow using Amazon S3, AWS Lambda, and Amazon Bedrock. This guide explains how documents can be uploaded, processed, analyzed with generative AI, and returned as useful summaries without managing servers.
Build a Serverless AI Document Analyzer with Amazon Bedrock and S3
Introduction
Documents contain valuable information, but manually reading and analyzing large numbers of documents can take significant time.
Generative AI can help automate this process. With AWS, we can combine Amazon S3, AWS Lambda, and Amazon Bedrock to create a serverless document-analysis workflow.
The basic idea is:
Upload Document → Store in S3 → Trigger Lambda → Analyze with AI → Generate Result
This architecture is useful for learning how cloud storage, serverless computing, and generative AI can work together.
What We Are Building
In this project, we will create a simple workflow where:
- A user uploads a document to an Amazon S3 bucket.
- The upload generates an event.
- AWS Lambda receives the event.
- Lambda retrieves the document information.
- Amazon Bedrock is used to perform an AI-powered analysis.
- The generated result can be stored back in Amazon S3.
The same architecture can be extended for document summarization, classification, question answering, and information extraction.
AWS Services Used
Amazon S3
Amazon S3 provides scalable object storage for documents and generated results.
We can organize objects using prefixes such as:
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documents/
results/
S3 also supports object tagging, which can be useful for categorizing and managing stored objects.
AWS Lambda
AWS Lambda runs code without requiring us to manage servers.
For this project, Lambda acts as the connection between S3 and the AI processing layer.
An S3 upload can trigger a Lambda function, allowing processing to happen automatically.
Amazon Bedrock
Amazon Bedrock provides access to foundation models through AWS APIs.
Instead of building and maintaining an AI model from scratch, developers can use supported foundation models for tasks such as summarization, classification, and text generation.
Architecture
The workflow can be represented as:
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User
|
v
Upload Document
|
v
+-------------------+
| Amazon S3 |
| Document Bucket |
+-------------------+
|
S3 Event
|
v
+-------------------+
| AWS Lambda |
| Processing Logic |
+-------------------+
|
v
+-------------------+
| Amazon Bedrock |
| Foundation Model |
+-------------------+
|
v
+-------------------+
| Amazon S3 |
| Analysis Result |
+-------------------+
Step 1: Create an S3 Bucket
Create an S3 bucket for storing uploaded documents.
For example:
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my-ai-documents
Inside the bucket, you can use prefixes such as:
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documents/
results/
Keep the bucket private unless public access is specifically required. AWS recommends keeping S3 Block Public Access enabled unless there is a specific reason to expose bucket content publicly.
Step 2: Upload a Document
Upload a sample document into:
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documents/
For example:
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documents/sample.pdf
The S3 upload can be configured to generate an event that invokes the Lambda function.
Step 3: Create the Lambda Function
Create a Lambda function that receives the S3 event.
The function can:
- Read the bucket name
- Read the uploaded object key
- Retrieve the document
- Extract the required content
- Prepare an AI prompt
- Send the request to Amazon Bedrock
- Store the generated result
A simplified Python structure could look like:
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def lambda_handler(event, context):
# Read S3 event
bucket = event["bucket"]
key = event["key"]
# Retrieve document
document = get_document(bucket, key)
# Prepare AI request
prompt = f"""
Analyze the following document and provide:
1. A short summary
2. Important points
3. Key topics
Document:
{document}
"""
# Send request to Amazon Bedrock
result = analyze_with_bedrock(prompt)
# Store result
save_result(bucket, key, result)
return {
"statusCode": 200,
"message": "Document analyzed successfully"
}
The exact implementation depends on the document type and the Bedrock model/API you choose.
Step 4: Create an AI Prompt
A good prompt makes the expected output clear.
For example:
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You are a document analysis assistant.
Analyze the provided document and return:
Summary:
Provide a concise summary.
Key Points:
List the most important points.
Topics:
Identify the major topics discussed.
Important Information:
Extract important facts that a user should know.
You can customize the prompt according to your application.
Step 5: Process the Result
After Amazon Bedrock generates the response, Lambda can save the result to another S3 location.
For example:
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results/sample-analysis.json
The output could contain:
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{
"summary": "Short document summary",
"key_points": [
"Important point 1",
"Important point 2",
"Important point 3"
],
"topics": [
"Artificial Intelligence",
"Cloud Computing"
]
}
Security Considerations
Security should be considered from the beginning.
1. Keep S3 Private
Avoid making document buckets publicly accessible unless required.
2. Use IAM Permissions
Give Lambda only the permissions it actually needs.
For example, the function may need permission to:
- Read documents from a specific S3 prefix
- Write results to a specific S3 prefix
- Invoke the required Bedrock API
Following least-privilege access reduces unnecessary permissions.
3. Protect Sensitive Documents
Do not place confidential information into logs or prompts unnecessarily.
Consider encryption, appropriate IAM policies, and careful logging when working with sensitive documents.
Possible Improvements
Once the basic workflow works, it can be extended significantly.
Document Summarization
Automatically create summaries for long documents.
Document Classification
Automatically classify documents into categories such as:
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Invoice
Resume
Report
Research Paper
Legal Document
Information Extraction
Extract structured information such as:
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Name
Date
Organization
Keywords
Important Numbers
Question Answering
Build a system where users can ask questions about uploaded documents.
Automated Document Processing
Additional AWS services can be introduced to create a larger production architecture with event-driven processing, monitoring, authentication, and APIs.
Why Serverless?
A serverless architecture is useful for this type of application because you don't need to manage traditional servers for the processing workflow.
The application can respond to document uploads and run processing only when needed.
This makes the architecture a good learning project for understanding:
- Cloud storage
- Event-driven architecture
- Serverless computing
- Generative AI
- IAM security
- AWS service integration
Final Thoughts
Combining Amazon S3, AWS Lambda, and Amazon Bedrock provides a practical way to learn how generative AI applications can be built on AWS.
The basic workflow is simple:
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S3
↓
Lambda
↓
Amazon Bedrock
↓
S3
From this starting point, you can build more advanced applications such as AI document assistants, automated report analyzers, knowledge-base systems, and intelligent document-processing pipelines.
The key lesson is that generative AI becomes even more useful when it is integrated with cloud services and real-world application workflows.
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