Reven AI: An all-encompassing platform for patients, healthcare providers, and staff to manage requests and payments.

Category: Healthcare

Services: Healthcare Data Analytics, Patient Engagement Platforms, Medical Imaging Solutions, Healthcare Workflow Automation, Compliance and Security Solutions, Custom Healthcare Application Development.

  • The cost of data extraction was reduced by approximately 35%.
  • Improved text extraction accuracy by 25%
  • Reduced data extraction time by 40%
  • A 95% reduction in data breach incidents was achieved 

About Reven AI

RevenAI is a healthcare-focused organization dedicated to revolutionizing the management of healthcare service requests, healthcare providers, and caregivers. The company’s commitment to advancing healthcare services is evident in its mission to track, manage, and prioritize critical elements within the healthcare domain. With a specialized focus on healthcare, RevenAI is dedicated to extracting data accurately and delivering innovative solutions that enhance the healthcare ecosystem.


  • Unstructured data formats needed an optimum extraction mechanism, including medical records, doctor’s notes, and patient correspondence.
  • Raven AI faced the challenges of securely extracting and processing text data while ensuring compliance with HIPAA regulations.
  • Raven AI required multilingual text extraction capabilities to ensure inclusivity and accuracy in data extraction.
  • Advanced text recognition and extraction capabilities are required to handle handwritten prescription data.
  • Needed implementation of entity recognition solutions to improve data extraction accuracy.

Proposed Solution & Architecture

  • Simform used AWS Textract to extract relevant information from unstructured healthcare data sources like medical records and correspondence.
  • We used AWS ECS Fargate to run Textract-powered extraction processes in isolated containers securely, ensuring HIPAA compliance.
  • Simform utilized AWS WAF to safeguard “RevenAI’s” text extraction system against common web attacks and to filter potentially harmful traffic.
  • AWS Amplify was used by “RevenAI” to develop and deploy secure, scalable applications for their text extraction solution.
  • Simform used Amazon S3 to store extracted text data in a secure, scalable, and highly available manner.
  • Our team used Amazon DynamoDB as a managed NoSQL database service to store and retrieve metadata and structured information for extracted text data.
  • Simform’s experts used AWS Lambda to execute code serverlessly for processing the extracted textual data.

Metrics for success

  • By adopting the hybrid approach, data extraction cost was reduced by 35%.
  • We improved text extraction accuracy by 25%, leveraging AWS Textract.
  • Reduced data extraction time by 40% through the implementation of advanced extraction algorithms and automation processes
  • A 95% reduction in data breach incidents was achieved by using AWS WAF, ensuring compliance with HIPAA and data security.

Architecture Diagram


AWS Services

  • AWS ECS Fargate- Our team used AWS ECS (Elastic Container Service) Fargate to provide a scalable and cost-effective containerized environment for running the text extraction processes. 
  • AWS Textract- We used AWS Textract’s OCR and document processing to extract healthcare data from medical records and patient forms.
  • AWS ECS Fargate- Our team used AWS ECS (Elastic Container Service) Fargate to provide a scalable and cost-effective containerized environment for running the text extraction processes. 
  • AWS Amplify – We used AWS Amplify to build and deploy secure, scalable web and mobile applications for efficient text extractions.
  • AWS WAF- Simform leveraged AWS WAF (Web Application Firewall) to safeguard the text extraction system against common web attacks and to filter out potentially malicious traffic.
  • Amazon S3- Our team leveraged Amazon S3 to store the extracted text data in a scalable, secure, and highly available manner. 
  • Amazon DynamodB- We used Amazon DynamoDB, a fully managed NoSQL database service, to store and retrieve structured metadata related to the extracted text data.
  • AWS Lambda- We used AWS Lambda to execute code without servers for processing extracted text data.
  • AWS IAM- Our team of experts used AWS IAM to ensure that only authorized personnel could access sensitive healthcare data, enhancing security and compliance with HIPAA regulations.
  • AWS CloudTrail– AWS CloudTrail improved API call and resource usage visibility, critical for healthcare data access monitoring, compliance, and security.
  • AWS CloudWatch- We used AWS CloudWatch to monitor text extraction and processing performance, ensuring accurate and efficient data extraction.
  • Amazon Pinpoint- Our team used Amazon Pinpoint for multilingual text extraction and improved patient communication through targeted correspondence.
  • Amazon KMS- Simform secured healthcare data by integrating Amazon KMS for key management, ensuring HIPAA compliance and patient record confidentiality.

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