
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
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Features and programs
Financing for AWS Marketplace purchases
Pricing
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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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.
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.jsonSubstitute 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 FilesÂ
- Input MIME type
- application/json, text/plain
Resources
Vendor resources
Support
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