This Guidance shows how to derive sustainability insights from utility bills using artificial intelligence and machine learning (AI/ML). Customers of public utilities can convert their invoices into structured data, automating the extraction of key data points from any utility invoice, normalizing the data across the various types of utilities, and gaining insights about where they are generating the highest emissions.
Architecture Diagram
Step 1
Any ingestion mechanism can be used to move invoice documents into Amazon Simple Storage Service (Amazon S3) for processing, such as Amazon API Gateway, Amazon Simple Email Service (Amazon SES), or an AWS Software Development Kit (SDKs).
Step 2
Invoice documents arrive in an Amazon S3 bucket, trigger event notifications in Amazon EventBridge, starting a processing workflow in AWS Step Functions.
Step 3
Customize pre-processing logic to use one or more features of Amazon Textract, such as the AnalyzeDocument and AnalyzeExpense APIs. Customers can call Amazon Textract API operations from within an AWS Lambda function.
Step 4
Amazon Textract performs the heavy lifting of converting invoice documents from their binary format into a machine-readable format used in post-processing.
Step 5
Customize the post-processing logic to consume and transform results from Amazon Textract. Custom templates can be applied here based on the utility provider identified in the document. Customers can use a Lambda function to process the results of a text detection request.
Step 6
Amazon DynamoDB is used to track the state of invoices as they move through the pipeline and store utility-specific templates used in post-processing.
Step 7
Standardized utility invoice data is stored in an output Amazon S3 bucket to integrate with downstream analytics capabilities such as Amazon QuickSight or a Data Lake on AWS solution where AWS Glue is used for data transformation.
Step 1
Any ingestion mechanism can be used to move invoice documents into Amazon Simple Storage Service (Amazon S3) for processing, such as Amazon API Gateway, Amazon Simple Email Service (Amazon SES), or an AWS Software Development Kit (SDKs).
Step 2
Invoice documents arrive in an Amazon S3 bucket, trigger event notifications in Amazon EventBridge, starting a processing workflow in AWS Step Functions.
Step 3
Customize pre-processing logic to use one or more features of Amazon Textract, such as the AnalyzeDocument and AnalyzeExpense APIs. Customers can call Amazon Textract API operations from within an AWS Lambda function.
Step 4
Amazon Textract performs the heavy lifting of converting invoice documents from their binary format into a machine-readable format used in post-processing.
Step 5
Customize the post-processing logic to consume and transform results from Amazon Textract. Custom templates can be applied here based on the utility provider identified in the document. Customers can use a Lambda function to process the results of a text detection request.
Step 6
Amazon DynamoDB is used to track the state of invoices as they move through the pipeline and store utility-specific templates used in post-processing.
Step 7
Standardized utility invoice data is stored in an output Amazon S3 bucket to integrate with downstream analytics capabilities such as Amazon QuickSight or a Data Lake on AWS solution where AWS Glue is used for data transformation.
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
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.
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Operational ExcellenceRead the Operational Excellence whitepaper
Standard metrics are used to monitor the health of the individual pipeline components, such as a Lambda function concurrency or error rate. The application state of any invoice or set of invoices can be queried in DynamoDB.
Amazon CloudWatch metrics, alarms, and dashboards can be customized to monitor the operational health and notify operators of faults.
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SecurityRead the Security whitepaper
Resources deployed by this Guidance are protected by AWS Identity and Access Management (IAM) policies and principles. Use least-privilege access and role-based access to grant operators permissions to modify resources, such as deploying an updated stack through AWS CloudFormation.
Data in this Guidance is protected through access controls implemented by IAM and Amazon S3 bucket policies. Stored data is encrypted with keys in AWS Key Management Service (AWS KMS).
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ReliabilityRead the Reliability whitepaper
The Guidance uses loosely coupled dependencies, such as EventBridge rules, to handle ingestion events from Amazon S3 and implement retries when a downstream quota is reached.
The Guidance is defined as infrastructure-as-code in an AWS Cloud Development Kit (AWS CDK) stack. AWS CDK stacks are deployed through CloudFormation for resilient change management that will automatically roll back if a fault is detected during deployment.
The Guidance uses AWS services such as Amazon S3 and DynamoDB for data storage. Both services provide out-of-box options for data backup and recovery, working immediately when the product is placed in service.
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Performance EfficiencyRead the Performance Efficiency whitepaper
This Guidance scales to meet the workload requirements of traffic patterns, using a queuing mechanism to control the rate at which invoices are processed. Customers can request an increase to Amazon Textract quotas so that invoice processing and their data velocity needs can be run in parallel.
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Cost OptimizationRead the Cost Optimization whitepaper
Serverless technologies were selected to reduce costs that are bound to the number of invoices processed. For the storage of binary invoice documents, customers can use Amazon S3 Intelligent Tiering or Amazon S3 Lifecycle policies to lower long-term storage costs or eliminate long-term storage of documents entirely.
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SustainabilityRead the Sustainability whitepaper
This Guidance uses Amazon S3 to store the invoice documents, which represent the largest data type of the Guidance. Customers can make small adjustments to achieve their ideal data storage using Amazon S3 Intelligent Tiering or Amazon S3 Lifecycle policies to reduce storage needs, as mentioned under Cost Optimization.
Implementation Resources
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
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Disclaimer
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