Sold by: MphasisÂ
Deployed on AWS
Solution extracts comprehensive conditional rules from official documents to validate claims & ensure compliance with specified guidelines.
Overview
This solution is a cutting-edge application for creating comprehensive rules across various domains, including claims validation, policy benefits, and subsidies. Leveraging Claude 3.5 Sonnet, it efficiently processes extensive documents to extract and formulate conditional rules from official texts.
The system employs a two-tier verification process: initial rule generation by Claude 3.5 Sonnet, followed by supervised optimization for refinement. This approach ensures high accuracy and reliability while minimizing errors. The application prioritizes secure handling of sensitive data throughout the process. It is ideal for insurance companies, healthcare providers, financial institutions, and government agencies, automating complex rule extraction, enhancing accuracy, and ensuring regulatory compliance.
By streamlining rule generation and management, it significantly improves efficiency and reduces the risk of human error in rule-based systems across various industries.
Highlights
- Enhanced Accuracy: Leverages Claude 3.5 Sonnet and a supervised verification process to generate a thorough and precise set of rules, reducing the risk of errors in claim validation.
- Two-Level Verification Process: 1)Initial rule generation using Claude 3.5 Sonnet, producing a preliminary set of rules. 2)Supervised Optimization: Supervision and review of the initial rule set to refine and optimize the final rules, ensuring higher precision and reliability.
- Regulatory Compliance: Ensures secure handling of sensitive documents and generated rules compliant with data protection regulations.
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $5.00/host/hour |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $5.00/host/hour |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $5.00/host/hour |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $5.00/host/hour |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $5.00/host/hour |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $5.00/host/hour |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $5.00/host/hour |
ml.m4.xlarge Inference (Batch) | Model inference on the ml.m4.xlarge instance type, batch mode | $5.00/host/hour |
ml.c5.4xlarge Inference (Batch) | Model inference on the ml.c5.4xlarge instance type, batch mode | $5.00/host/hour |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $5.00/host/hour |
Vendor refund policy
Currently, we do not support refunds, but you can cancel your subscription to the service at any time.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Enhanced accuracy, two-level verification and regulatory compliance, all these highlighting features make is solution a cutting edge product option for creating comprehensive rules across various domains, including claims validation, policy benefits, and subsidies.
Additional details
Inputs
- Summary
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The input is provided as a zip file which should mandatorily contain two elements: a) files/ folder containing the files you want to process b) a json file for credentials which will containe access key, id and region name (for calling the anthropic model)
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The name of the zip file should be saved as "Input_data.zip" to comply with the logic written for it.
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The files that are to be processed should only be in either docx or pdf format.
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- Limitations for input type
- Input file should only be in zip format and should be saved as "Input_data.zip". Files to be processed has to be mandatorily in either docx or pdf format only.
- Input MIME type
- application/json, application/zip
https://github.com/Mphasis-ML-Marketplace/Business-rule-Extraction-using-GenAI/blob/9451872d91a453ec6eead77a2fafd7260b7ddd35/Input_data.zip
https://github.com/Mphasis-ML-Marketplace/Business-rule-Extraction-using-GenAI/blob/9451872d91a453ec6eead77a2fafd7260b7ddd35/Input_data.zip
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Text document | The input data consists of documents in either pdf or docx format. These documents contain business information and essential for forming policies and checking for fraud prevention or claim validation. | Type: FreeText | Yes |
pdf or docx file | The input data consists of documents in either pdf or docx format. These documents contain business information and essential for forming policies and checking for fraud prevention or claim validation. | Type: FreeText | Yes |
Credentials | This file contains all the essential information to connect to a model (claude 3.5 sonnet in this case) to process the documents for generating business rules out of them. | Type: FreeText | Yes |
json file | This file contains all the essential information to connect to a model (claude 3.5 sonnet in this case) to process the documents for generating business rules out of them. | Type: FreeText | Yes |
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