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    Email Intent Identifier for Insurance

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    Sold by: Mphasis 
    Deployed on AWS
    This Natural Language Processing based solution helps in identifying multiple intents of inbound life insurance email queries.

    Overview

    Mphasis DeepInsights Email Intent is Machine Learning and Natural Language Processing based solution and helps in identifying multiple intents of inbound life insurance email queries. The model can be used to automatically infer the customer intent in a support inbox and draft dynamic responses based on that. The solution is currently configured for life insurance queries and can be extended to other domains.

    Highlights

    • The solution provides a contextual response to the inbound emails depending on its contents. Response can be static or dynamic depending upon the type of content in the email.
    • The solution can be used to connect directly to customer support mailboxes and enable real time responses. The solution can be used to route the email to the right team or resources based on the intent(s) identified. The solution currently caters to Life Insurance queries. It can be configured for other domains and industries as well.
    • Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!

    Details

    Delivery method

    Latest version

    Deployed on AWS

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    Pricing

    Email Intent Identifier for Insurance

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    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.

    Usage costs (52)

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    Dimension
    Description
    Cost/host/hour
    ml.m5.large Inference (Batch)
    Recommended
    Model inference on the ml.m5.large instance type, batch mode
    $16.00
    ml.m5.large Inference (Real-Time)
    Recommended
    Model inference on the ml.m5.large instance type, real-time mode
    $8.00
    ml.m4.4xlarge Inference (Batch)
    Model inference on the ml.m4.4xlarge instance type, batch mode
    $16.00
    ml.m5.4xlarge Inference (Batch)
    Model inference on the ml.m5.4xlarge instance type, batch mode
    $16.00
    ml.m4.16xlarge Inference (Batch)
    Model inference on the ml.m4.16xlarge instance type, batch mode
    $16.00
    ml.m5.2xlarge Inference (Batch)
    Model inference on the ml.m5.2xlarge instance type, batch mode
    $16.00
    ml.p3.16xlarge Inference (Batch)
    Model inference on the ml.p3.16xlarge instance type, batch mode
    $16.00
    ml.m4.2xlarge Inference (Batch)
    Model inference on the ml.m4.2xlarge instance type, batch mode
    $16.00
    ml.c5.2xlarge Inference (Batch)
    Model inference on the ml.c5.2xlarge instance type, batch mode
    $16.00
    ml.p3.2xlarge Inference (Batch)
    Model inference on the ml.p3.2xlarge instance type, batch mode
    $16.00

    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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    Legal

    Vendor terms and conditions

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    Usage information

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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:
    Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference  .
    Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI  .
    Version release notes

    Bug Fixes and Performance Improvement

    Additional details

    Inputs

    Summary

    Input

    • This solution works with outlook messages. When outlook messages of msg file format are parsed, this solution provides a response depending upon the intent of the mail. • Supported content type: application/json. • Sample input file will be converted into application json using following commands:

    file_path = ('sample_mail.msg') input_mail = extract_msg.Message(file_path)

    • Sample input:

    { 'email_sender': 'NextLabs Testmail <nextlabs.testmail@mphasis.com>', 'email_body': 'Hi Team,Please tell me how to download the policy document', 'email_subject': 'Query' }

    Output

    • Content type: application/json. • Sample output:

    { '0': { 'Response': 'Hello NextLabs Testmail, The policy document can be downloaded from the website, you would require to login to the e-service portal and click on the policy number and then the download button.', 'Intents': 'Download_policy' } }

    • Cancellation, death claim, download policy, fund switch, maturity claim, payment, survival benefit, update number, sum assured, due date, premium amount, last date and maturity date are the set of intents that solution can identify.

    Invoking endpoint

    AWS CLI Command

    If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it

    aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.json --content-type application/json --accept application/json out.json

    Substitute the following parameters:

    • "endpoint-name" - name of the inference endpoint where the model is deployed
    • input.json - input msg file to do the inference on
    • application/json - MIME type of the given input image (above)
    • out.json - filename where the inference results are written to

    Resources

    • Sample Input Files  • Sample Notebook  • Sample Output Files 

    Input MIME type
    application/json, text/plain
    See Input Summary
    See Input Summary

    Support

    Vendor support

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