Construct an AI-powered automated summarization system with Amazon Bedrock and Amazon Transcribe utilizing Terraform

Extracting significant insights from unstructured information presents important challenges for a lot of organizations. Assembly recordings, buyer interactions, and interviews include invaluable enterprise intelligence that continues to be largely inaccessible because of the prohibitive time and useful resource prices of handbook overview. Organizations often battle to effectively seize and use key data from these interactions, leading to not solely productiveness gaps but additionally missed alternatives to make use of essential decision-making data.

This submit introduces a serverless assembly summarization system that harnesses the superior capabilities of Amazon Bedrock and Amazon Transcribe to rework audio recordings into concise, structured, and actionable summaries. By automating this course of, organizations can reclaim numerous hours whereas ensuring key insights, motion gadgets, and choices are systematically captured and made accessible to stakeholders.

Many enterprises have standardized on infrastructure as code (IaC) practices utilizing Terraform, usually as a matter of organizational coverage. These practices are sometimes pushed by the necessity for consistency throughout environments, seamless integration with present steady integration and supply (CI/CD) pipelines, and alignment with broader DevOps methods. For these organizations, having AWS options applied with Terraform helps them preserve governance requirements whereas adopting new applied sciences. Enterprise adoption of IaC continues to develop quickly as organizations acknowledge the advantages of automated, version-controlled infrastructure deployment.

This submit addresses this want by offering a whole Terraform implementation of a serverless audio summarization system. With this answer, organizations can deploy an AI-powered assembly summarization answer whereas sustaining their infrastructure governance requirements. The enterprise advantages are substantial: lowered assembly follow-up time, improved data sharing, constant motion merchandise monitoring, and the flexibility to look throughout historic assembly content material. Groups can concentrate on performing upon assembly outcomes reasonably than struggling to doc and distribute them, driving sooner decision-making and higher organizational alignment.

What are Amazon Bedrock and Amazon Transcribe?

Amazon Bedrock is a completely managed service that gives a selection of high-performing basis fashions (FMs) from main AI firms like AI21 Labs, Anthropic, Cohere, DeepSeek, Luma, Meta, Mistral AI, poolside (coming quickly), Stability AI, TwelveLabs (coming quickly), Author, and Amazon Nova by means of a single API, together with a broad set of capabilities to construct generative AI functions with safety, privateness, and accountable AI. With Amazon Bedrock, you possibly can experiment with and consider prime FMs on your use case, customise them along with your information utilizing strategies corresponding to fine-tuning and Retrieval Augmented Era (RAG), and construct brokers that execute duties utilizing your enterprise programs and information sources.

Amazon Transcribe is a completely managed, automated speech recognition (ASR) service that makes it simple for builders so as to add speech to textual content capabilities to their functions. It’s powered by a next-generation, multi-billion parameter speech FM that delivers high-accuracy transcriptions for streaming and recorded speech. 1000’s of consumers throughout industries use it to automate handbook duties, unlock wealthy insights, enhance accessibility, and enhance discoverability of audio and video content material.

Answer overview

Our complete audio processing system combines highly effective AWS providers to create a seamless end-to-end answer for extracting insights from audio content material. The structure consists of two foremost parts: a user-friendly frontend interface that handles buyer interactions and file uploads, and a backend processing pipeline that transforms uncooked audio into beneficial, structured data. This serverless structure facilitates scalability, reliability, and cost-effectiveness whereas delivering insightful AI-driven evaluation capabilities with out requiring specialised infrastructure administration.

The frontend workflow consists of the next steps:

1. Customers add audio information by means of a React-based frontend delivered globally utilizing Amazon CloudFront.
2. Amazon Cognito supplies safe authentication and authorization for customers.
3. The appliance retrieves assembly summaries and statistics by means of AWS AppSync GraphQL API, which invokes AWS Lambda capabilities to question.

The processing consists of the next steps:

1. Audio information are saved in an Amazon Easy Storage Service (Amazon S3) bucket.
2. When an audio file is uploaded to Amazon S3 within the audio/{user_id}/ prefix, an S3 occasion notification sends a message to an Amazon Easy Queue Service (Amazon SQS) queue.
3. The SQS queue triggers a Lambda operate, which initiates the processing workflow.
4. AWS Step Features orchestrates the whole transcription and summarization workflow with built-in error dealing with and retries.
5. Amazon Transcribe converts speech to textual content with excessive accuracy.
6. makes use of an FM (particularly Anthropic’s Claude) to generate complete, structured summaries.
7. Outcomes are saved in each Amazon S3 (uncooked information) and Amazon DynamoDB (structured information) for persistence and fast retrieval.

For extra safety, AWS Id and Entry Administration helps handle identities and entry to AWS providers and assets.

The next diagram illustrates this structure.

This structure supplies a number of key advantages:

• Totally serverless – Automated scaling and no infrastructure to handle
• Occasion-driven – Actual-time responses from parts primarily based on occasions
• Resilient – Constructed-in error dealing with and retry mechanism
• Safe – Authentication, authorization, and encryption all through
• Value-effective – Pay-per-use worth mannequin
• Globally accessible – Content material supply optimized for customers worldwide
• Extremely extensible – Seamless integration with further providers

Let’s stroll by means of the important thing parts of our answer in additional element.

Venture construction

Our assembly audio summarizer challenge follows a construction with frontend and backend parts:

sample-meeting-audio-summarizer-in-terraform/                                         
├── backend/                                                                          
│   ├── capabilities/                           # Lambda operate code                   
│   │   ├── audio-processing/                # Audio processing capabilities             
│   │   ├── authentication/                  # Authentication capabilities               
│   │   ├── data-access/                     # Knowledge entry capabilities                  
│   │   ├── queue-processing/                # SQS queue processing capabilities         
│   │   ├── summarization/                   # Summarization capabilities                
│   │   ├── transcription/                   # Transcription capabilities                
│   │   └── zipped/                          # Zipped Lambda capabilities for deployment 
│   └── terraform/                           # Infrastructure as Code                 
│       ├── modules/                         # Terraform modules                      
│       │   ├── api/                         # AppSync GraphQL API                    
│       │   ├── auth/                        # Cognito authentication                 
│       │   ├── compute/                     # Lambda capabilities                       
│       │   ├── messaging/                   # SQS queues and S3 notifications        
│       │   ├── community/                     # CloudFront and S3 web site              
│       │   ├── orchestration/               # Step Features                         
│       │   ├── queue-processor/             # Queue processing Lambda                
│       │   └── storage/                     # S3 and DynamoDB                        
│       ├── foremost.tf                          # Major Terraform configuration           
│       ├── outputs.tf                       # Output values                          
│       ├── variables.tf                     # Enter variables                        
│       └── terraform.tfvars                 # Variable values                        
├── docs/                                    # Documentation and structure diagrams
├── frontend/                                # React internet software                  
│   ├── public/                              # Public belongings                          
│   └── src/                                 # React software supply               
│       ├── parts/                      # React parts                       
│       ├── graphql/                         # GraphQL queries and mutations          
│       ├── pages/                           # Web page parts                        
│       └── providers/                        # Service integrations                   
└── scripts/                                 # Deployment and utility scripts         
├── deploy.sh                                # Major deployment script                 
└── zip-lambdas.sh                           # Script to zip all backend lambdas  

Infrastructure setup Terraform

Our answer makes use of Terraform to outline and provision the AWS infrastructure in a constant and repeatable manner. The primary Terraform configuration orchestrates the varied modules. The next code reveals three of them:

# Compute Module - Lambda capabilities
module "compute" {
  supply = "./modules/compute"
  
  aws_region                        = var.aws_region
  aws_account                       = information.aws_caller_identity.present.account_id
  meeting_statistics_table_name     = var.meeting_statistics_table_name
  meeting_summaries_table_name      = var.meeting_summaries_table_name
  cognito_user_pool_id              = module.auth.cognito_user_pool_id
  iam_roles                         = module.auth.iam_roles
  storage_bucket                    = module.storage.storage_bucket
  model_id                          = var.model_id
  inference_profile_prefix          = var.inference_profile_prefix
}

# Orchestration Module - Step Features
module "orchestration" {
  supply = "./modules/orchestration"
  
  aws_region                              = var.aws_region
  aws_account                             = information.aws_caller_identity.present.account_id
  storage_bucket                          = module.storage.storage_bucket
  iam_roles                               = module.auth.iam_roles
  lambda_functions                        = module.compute.lambda_functions
}

# Queue Processor Module - ProcessTranscriptionQueueFunction Lambda
module "queue_processor" {
  supply = "./modules/queue-processor"
  
  storage_bucket                    = module.storage.storage_bucket
  state_machine_arn                 = module.orchestration.state_machine_arn
  lambda_function_transcription_role = module.auth.iam_roles.lambda_function_transcription_role
  
  depends_on = [
    module.storage,
    module.orchestration
  ]
}

Audio processing workflow

The core of our answer is a Step Features workflow that orchestrates the processing of audio information. The workflow handles language detection, transcription, summarization, and notification in a resilient manner with correct error dealing with.

Amazon Bedrock for summarization

The summarization element is powered by Amazon Bedrock, which supplies entry to state-of-the-art FMs. Our answer makes use of Anthropic’s Claude 3.7 Sonnet model 1 to generate complete assembly summaries:

immediate = f"""Even when it's a uncooked transcript of a gathering dialogue, missing clear construction and context and containing a number of audio system, incomplete sentences, and tangential subjects, PLEASE PROVIDE a transparent and thorough evaluation as detailed as doable of this dialog. DO NOT miss any data. CAPTURE as a lot data as doable. Use bullet factors as an alternative of dashes in your abstract.
IMPORTANT: For ALL part headers, use plain textual content with NO markdown formatting (no #, ##, **, or * symbols). Every part header needs to be in ALL CAPS adopted by a colon. For instance: "TITLE:" not "# TITLE" or "## TITLE".

CRITICAL INSTRUCTION: DO NOT use any markdown formatting symbols like #, ##, **, or * in your response, particularly for the TITLE part. The TITLE part MUST begin with "TITLE:" and never "# TITLE:" or any variation with markdown symbols.

FORMAT YOUR RESPONSE EXACTLY AS FOLLOWS:

TITLE: Give the assembly a brief title 2 or 3 phrases that's associated to the general context of the assembly, discover a distinctive title such an organization title or stakeholder and embrace it within the title      

TYPE: Relying on the context of the assembly, the dialog, the subject, and dialogue, ALWAYS assign a sort of assembly to this abstract. Allowed Assembly sorts are: Consumer assembly, Workforce assembly, Technical assembly, Coaching Session, Standing Replace, Brainstorming Session, Assessment Assembly, Exterior Stakeholder Assembly, Choice Making Assembly, and Downside Fixing Assembly. That is essential, do not overlook this.

STAKEHOLDERS:
Present a listing of the individuals within the assembly, their firm, and their corresponding roles. If the title will not be supplied or not understood, please substitute the title with the phrase 'Not said'. If a speaker doesn't introduce themselves, then do not embrace them within the STAKEHOLDERS part.  

CONTEXT:
present a 10-15 abstract or context sentences with the next data: Major purpose for contact, Decision supplied, Ultimate final result, contemplating all the data above

MEETING OBJECTIVES:
present all of the targets or objectives of the assembly. Be thorough and detailed.

CONVERSATION DETAILS:
Buyer's foremost issues/requests
Options mentioned
Necessary data verified
Selections made

KEY POINTS DISCUSSED (Elaborate on every level, if relevant):
Record all important subjects and points
Necessary particulars or numbers talked about
Any insurance policies or procedures defined
Particular requests or exceptions

ACTION ITEMS & NEXT STEPS (Elaborate on every level, if relevant):
What the shopper must do:
Rapid actions required
Future steps to take
Necessary dates or deadlines
What the corporate will do (Elaborate on every level, if relevant):
Processing or dealing with steps
Comply with-up actions promised
Timeline for completion

ADDITIONAL NOTES (Elaborate on every level, if relevant):
Any notable points or issues
Comply with-up suggestions
Necessary reminders

TECHNICAL REQUIREMENTS & RESOURCES (Elaborate on every level, if relevant):
Programs or instruments mentioned/wanted
Technical specs talked about
Required entry or permissions
Useful resource allocation particulars

Frontend implementation

The frontend is constructed with React and supplies the next options:

• Person authentication and authorization utilizing Amazon Cognito
• Audio file add interface with progress indicators
• Abstract viewing with formatted sections (stakeholders, key factors, motion gadgets)
• Search performance throughout assembly summaries
• Assembly statistics visualization

The frontend communicates with the backend by means of the AWS AppSync GraphQL API, which supplies a unified interface for information operations.

Safety concerns

Safety is a prime precedence in our answer, which we handle with the next measures:

• Person authentication is dealt with by Amazon Cognito
• API entry is secured with Amazon Cognito person swimming pools
• S3 bucket entry is restricted to authenticated customers
• IAM roles comply with the precept of least privilege
• Knowledge is encrypted at relaxation and in transit
• Step Features present safe orchestration with correct error dealing with

Advantages of utilizing Amazon Bedrock

Amazon Bedrock presents a number of key benefits for our assembly summarization system:

• Entry to state-of-the-art mannequins – Amazon Bedrock supplies entry to main FMs like Anthropic’s Claude 3.7 Sonnet model 1, which delivers high-quality summarization capabilities with out the necessity to practice customized fashions.
• Totally managed integration – Amazon Bedrock integrates seamlessly with different AWS providers, permitting for a completely serverless structure that scales mechanically with demand.
• Value-efficiency – On-Demand pricing means you solely pay for the precise processing time, making it cost-effective for variable workloads.
• Safety and compliance – Amazon Bedrock maintains information privateness and safety, ensuring delicate assembly content material stays protected inside your AWS surroundings.
• Customizable prompts – The flexibility to craft detailed prompts permits for tailor-made summaries that extract precisely the data your group wants from conferences. Amazon Bedrock additionally supplies immediate administration and optimization, in addition to the playground for fast prototyping.
• Multilingual support – Amazon Bedrock can course of content material in a number of languages, making it appropriate for world organizations.
• Lowered improvement time – Pre-trained fashions decrease the necessity for in depth AI improvement experience and infrastructure.
• Steady enchancment – Amazon Bedrock supplies a mannequin selection, and the person can replace the present fashions with a single string change.

Stipulations

Earlier than implementing this answer, be sure to have:

Within the following sections, we stroll by means of the steps to deploy the assembly audio summarizer answer.

Clone the repository

First, clone the repository containing the Terraform code:

git clone https://github.com/aws-samples/sample-meeting-audio-summarizer-in-terraform
cd sample-meeting-audio-summarizer-in-terraform

Configure AWS credentials

Ensure your AWS credentials are correctly configured. You should utilize the AWS CLI to arrange your credentials:

aws configure --profile meeting-summarizer

You can be prompted to enter your AWS entry key ID, secret entry key, default AWS Area, and output format.

Set up frontend dependencies

To arrange the frontend improvement surroundings, navigate to the frontend listing and set up the required dependencies:

cd frontend
npm set up

Create configuration information

Transfer to the terraform listing:

cd ../backend/terraform/  

Replace the terraform.tfvars file within the backend/terraform listing along with your particular values. This configuration provides values for the variables beforehand outlined within the variables.tf file.

You may customise different variables outlined in variables.tf in keeping with your wants. Within the terraform.tfvars file, you present precise values for the variables declared in variables.tf, so you possibly can customise the deployment with out modifying the core configuration information:

aws_region                              = "us-east-1"                                  
aws_profile                             = "YOUR-AWS-PROFILE"                           
surroundings                             = "prod"                                       
app_name                                = "meeting-audio-summarizer"                   
dynamodb_read_capacity                  = 5                                            
dynamodb_write_capacity                 = 5                                            
cognito_allowed_email_domains           = ["example.com"]                              
model_id                                = "anthropic.claude-3-7-sonnet-20250219-v1:0"  
inference_profile_prefix                = "us"                                         
frontend_bucket_name                    = "a-unique-bucket-name"                       
storage_bucket                          = "a-unique-bucket-name"                       
cognito_domain_prefix                   = "meeting-summarizer"                         
meeting_statistics_table_name           = "MeetingStatistics"                          
meeting_summaries_table_name            = "MeetingSummaries"  

For a-unique-bucket-name, select a novel title that’s significant and is sensible to you.

Initialize and apply Terraform

Navigate to the terraform listing and initialize the Terraform surroundings:

terraform init

To improve the beforehand chosen plugins to the latest model that complies with the configuration’s model constraints, use the next command:

terraform init -upgrade

This may trigger Terraform to disregard picks recorded within the dependency lock file and take the latest accessible model matching the configured model constraints.

Assessment the deliberate modifications:

terraform plan

Apply the Terraform configuration to create the assets:

terraform apply

When prompted, enter sure to verify the deployment. You may run terraform apply -auto-approve to skip the approval query.

Deploy the answer

After the backend deployment is full, deploy the whole answer utilizing the supplied deployment script:

cd ../../scripts
sudo chmod +x deploy.sh
./deploy.sh

This script handles the whole deployment course of, together with:

• Deploying the backend infrastructure utilizing Terraform
• Robotically configuring the frontend with backend useful resource data
• Constructing and deploying the frontend software
• Organising CloudFront distribution
• Invalidating the CloudFront cache to ensure the most recent content material is served

Confirm the deployment

After the whole answer (each backend and frontend) is deployed, in your terminal it’s best to see one thing just like the next textual content:

Deployment full! :)

============================================================================
Your app is obtainable at: https://d1e5vh2t5qryy2.cloudfront.web.
============================================================================

The CloudFront URL (*.cloudfront.web/) is exclusive, so yours won’t be the identical.

Enter the URL into your browser to open the applying. You will note a login web page like the next screenshot. It’s essential to create an account to entry the applying.

Begin by importing a file:

View generated summaries in a structured format:

See assembly statistics:

Clear up

To cleanup the answer you have to run this command.

terraform destroy

This command will utterly take away the AWS assets provisioned by Terraform in your surroundings. When executed, it can show an in depth plan displaying the assets that will likely be destroyed, and immediate for affirmation earlier than continuing. The method could take a number of minutes because it systematically removes infrastructure parts within the appropriate dependency order.

Bear in mind to confirm the destruction is full by checking your AWS Console to ensure no billable assets stay energetic.

Value concerns

When implementing this answer, it’s vital to grasp the associated fee implications of every element. Let’s analyze the prices primarily based on a sensible utilization situation, primarily based on the next assumptions:

• 50 hours of audio processing per thirty days
• Common assembly size of half-hour
• 100 energetic customers accessing the system
• 5 million API queries per thirty days

The vast majority of the associated fee comes from Amazon Transcribe (roughly 73% of complete price at $72.00), with AWS AppSync being the second largest price element (roughly 20% at $20.00). Regardless of offering the core AI performance, Amazon Bedrock prices roughly 3% of complete at $3.00, and DynamoDB, CloudFront, Lambda, Step Features, Amazon SQS, and Amazon S3 make up the remaining 4%.

We will reap the benefits of the next price optimization alternatives:

• Implement audio compression to scale back storage and processing prices
• Use Amazon Transcribe Medical for medical conferences (if relevant) for greater accuracy
• Implement caching methods for often accessed summaries to scale back AppSync and DynamoDB prices
• Take into account reserved capability for DynamoDB if utilization patterns are predictable

The next desk summarizes these costs. Refer the AWS pricing pages for every service to study extra in regards to the AWS pricing mannequin.

Subsequent steps

The subsequent part of our assembly summarization answer will incorporate a number of superior AI applied sciences to ship better enterprise worth. Amazon Sonic Mannequin can enhance transcription accuracy by higher dealing with a number of audio system, accents, and technical terminology—addressing a key ache level for world organizations with various groups. In the meantime, Amazon Bedrock Flows can improve the system’s analytical capabilities by implementing automated assembly categorization, role-based abstract customization, and integration with company data bases to offer related context. These enhancements may help organizations extract actionable insights that might in any other case stay buried in dialog.

The addition of real-time processing capabilities helps groups see key factors, motion gadgets, and choices as they emerge throughout conferences, enabling quick clarification and decreasing follow-up questions. Enhanced analytics performance monitor patterns throughout a number of conferences over time, giving administration visibility into communication effectiveness, decision-making processes, and challenge progress. By integrating with present productiveness instruments like calendars, day by day agenda, activity administration programs, and communication providers, this answer makes positive that assembly intelligence flows immediately into day by day workflows, minimizing handbook switch of data and ensuring essential insights drive tangible enterprise outcomes throughout departments.

Conclusion

Our assembly audio summarizer combines AWS serverless applied sciences with generative AI to unravel a essential productiveness problem. It mechanically transcribes and summarizes conferences, saving organizations hundreds of hours whereas ensuring insights and motion gadgets are systematically captured and shared with stakeholders.

The serverless structure scales effortlessly with fluctuating assembly volumes, prices simply $0.98 per assembly on common, and minimizes infrastructure administration and upkeep overhead. Amazon Bedrock supplies enterprise-grade AI capabilities with out requiring specialised machine studying experience or important improvement assets, and the Terraform-based infrastructure as code permits fast deployment throughout environments, customization to satisfy particular organizational necessities, and seamless integration with present CI/CD pipelines.

As the sector of generative AI continues to evolve and new, better-performing fashions develop into accessible, the answer’s capacity to carry out its duties will mechanically enhance on efficiency and accuracy with out further improvement effort, enhancing summarization high quality, language understanding, and contextual consciousness. This makes the assembly audio summarizer an more and more beneficial asset for contemporary companies trying to optimize assembly workflows, improve data sharing, and enhance organizational productiveness.

Further assets

Confer with Amazon Bedrock Documentation for extra particulars on mannequin choice, immediate engineering, and API integration on your generative AI functions. Moreover, see Amazon Transcribe Documentation for details about the speech-to-text service’s options, language help, and customization choices for attaining correct audio transcription. For infrastructure deployment wants, see Terraform AWS Supplier Documentation for detailed explanations of useful resource sorts, attributes, and configuration choices for provisioning AWS assets programmatically. To reinforce your infrastructure administration expertise, see Finest practices for utilizing the Terraform AWS Supplier, the place yow will discover really helpful approaches for module group, state administration, safety configurations, and useful resource naming conventions that may assist be certain that your AWS infrastructure deployments stay scalable and maintainable.

Concerning the authors

Dunieski Otano is a Options Architect at Amazon Net Providers primarily based out of Miami, Florida. He works with World Broad Public Sector MNO (Multi-Worldwide Organizations) prospects. His ardour is Safety, Machine Studying and Synthetic Intelligence, and Serverless. He works together with his prospects to assist them construct and deploy excessive accessible, scalable, and safe options. Dunieski holds 14 AWS certifications and is an AWS Golden Jacket recipient. In his free time, one can find him spending time together with his household and canine, watching an amazing film, coding, or flying his drone.

Joel Asante, an Austin-based Options Architect at Amazon Net Providers (AWS), works with GovTech (Authorities Expertise) prospects. With a powerful background in information science and software improvement, he brings deep technical experience to creating safe and scalable cloud architectures for his prospects. Joel is obsessed with information analytics, machine studying, and robotics, leveraging his improvement expertise to design revolutionary options that meet advanced authorities necessities. He holds 13 AWS certifications and enjoys household time, health, and cheering for the Kansas Metropolis Chiefs and Los Angeles Lakers in his spare time.

Ezzel Mohammed is a Options Architect at Amazon Net Providers (AWS) primarily based in Dallas, Texas. He works on the Worldwide Organizations staff throughout the World Broad Public Sector, collaborating intently with UN companies to ship revolutionary cloud options. With a Pc Science background, Ezzeldien brings deep technical experience in system design, serving to prospects architect and deploy extremely accessible and scalable options that meet worldwide compliance necessities. He holds 9 AWS certifications and is obsessed with making use of AI Engineering and Machine Studying to deal with world challenges. In his free time, he enjoys occurring walks, watching soccer with family and friends, taking part in volleyball, and studying tech articles.