Expedite your Genesys Cloud Amazon Lex bot design with the Amazon Lex automated chatbot designer

The rise of synthetic intelligence (AI) has created alternatives to enhance the shopper expertise within the contact heart house. Machine studying (ML) applied sciences regularly enhance and energy the contact heart buyer expertise by offering options for capabilities like self-service bots, reside name analytics, and post-call analytics. Self-service bots built-in along with your name heart can assist you obtain decreased wait instances, clever routing, decreased time to decision by way of self-service capabilities or information assortment, and improved web promoter scores (NPS). Some examples embody a buyer calling to test on the standing of an order and receiving an replace from a bot, or a buyer needing to submit a renewal for a license and the chatbot amassing the mandatory data, which it fingers over to an agent for processing.

With Amazon Lex bots, you need to use conversational AI capabilities to allow these capabilities inside your name heart. Amazon Lex makes use of computerized speech recognition (ASR) and pure language understanding (NLU) to grasp the shopper’s wants and help them on their journey.

Genesys Cloud (an omni-channel orchestration and buyer relationship platform) offers a contact heart platform in a public cloud mannequin that permits fast and easy integration of AWS Contact Heart Intelligence (AWS CCI) to rework the trendy contact heart from a value heart right into a revenue heart. As a part of AWS CCI, Genesys Cloud integrates with Amazon Lex, which permits self-service, clever routing, and information assortment capabilities.

When exploring AWS CCI capabilities with Amazon Lex and Genesys Cloud, chances are you’ll be uncertain of the place to begin in your bot design journey. To help those that could also be beginning with a clean canvas, Amazon Lex offers the Amazon Lex automated chatbot designer. The automated chatbot designer makes use of ML to offer an preliminary bot design you can then refine and launch conversational experiences sooner based mostly in your present name transcripts. With the automated chatbot designer, Amazon Lex prospects and companions have an easy and intuitive manner of designing chatbots and might scale back bot design time from weeks to hours. Nevertheless, the automated chatbot designer requires transcripts to be in a sure format that isn’t aligned to Genesys Cloud transcript exports.

On this put up, we present how one can implement an structure utilizing Amazon EventBridge, Amazon Easy Storage Service (Amazon S3), and AWS Lambda to routinely acquire, rework, and cargo your Genesys name transcripts within the required format for the Amazon Lex automated chatbot designer. You may then run the automated chatbot designer in your transcripts, be given suggestions for bot design, and streamline your bot design journey.

Resolution overview

The next diagram illustrates the answer structure.

The answer workflow consists of the next steps:

1. Genesys Cloud sends iterative transcripts occasions to your EventBridge occasion bus.
2. Lambda receives the iterative transcripts from EventBridge, determines when a dialog is full, and invokes the Transcript API inside Genesys Cloud and drops the total transcript in an S3 bucket.
3. When a brand new full transcript is uploaded to Amazon S3, Lambda converts the Genesys Cloud formatted transcript into the required format for the Amazon Lex automated chatbot designer and copies it to an S3 bucket.
4. The Amazon Lex automated chatbot designer makes use of ML to construct an preliminary bot design based mostly on the supplied Genesys Cloud transcripts.

Conditions

Earlier than you deploy the answer, you could full the next stipulations:

1. Arrange your Genesys Cloud CX account and make it possible for you’ll be able to log in. For extra data on establishing your account, discuss with the Genesys documentation.
2. Be sure that the fitting permissions are set for enabling and publishing transcripts from Genesys. For extra data on establishing the required permissions, discuss with Roles and permissions overview.
3. If PCI and PII encryption is required for transcription, make certain it’s arrange in Genesys. For extra data on establishing the required permissions, discuss with Are interaction transcripts encrypted when stored in the cloud.
4. Arrange an AWS account with the suitable permissions.

Deploy the Genesys EventBridge integration

To allow the EventBridge integration with Genesys Cloud, full the next steps:

1. Log in to the Genesys Cloud environment.
2. Select Admin, Integrations, Add Integrations, and Amazon EventBridge Supply.
3. On the Configuration tab, present the next data:
   1. For AWS Account ID, enter your AWS account ID.
   2. For AWS Account Area, enter the Area the place you need EventBridge to be arrange.
   3. For Occasion Supply Suffix, enter a suffix (for instance, genesys-eb-poc-demo).
4. Save your configuration.
   
5. On the EventBridge console, select Integration within the navigation pane, then select Companion occasion sources.

There ought to be an occasion supply listed with a reputation like aws.accomplice/genesys.com/…/genesys-eb-poc-demo.

6. Choose the accomplice occasion supply and select Affiliate with occasion bus.

The standing modifications from Pending to Energetic. This units up the EventBridge configuration for Genesys.

Subsequent, you arrange OAuth2 credentials in Genesys Cloud for authorizing the API name to get the ultimate transcript.

7. Navigate to the Genesys Cloud occasion.
8. Select Admin, Integrations, and OAuth.
9. Select Add Shopper.
10. On the Shopper Particulars tab, present the next data:
    1. For App Identify, enter a reputation (for instance, TranscriptInvoke-creds).
    2. For Grant Sorts, choose Shopper Credentials.

Ensure you’re utilizing the fitting function that has entry to invoke the Transcribe APIs.

11. Select Save.

This generates new values for Shopper ID and Shopper Secret. Copy these values to make use of within the subsequent part, the place you configure the template for the answer.

Deploy the answer

After you could have arrange the Genesys EventBridge integration, you may deploy an AWS Serverless Utility Mannequin (AWS SAM) template, which deploys the rest of the structure. To deploy the answer in your account, full the next steps:

1. Set up AWS SAM if not put in already. For directions, discuss with Putting in the AWS SAM CLI.
2. Obtain the GitHub repo and unzip to your listing.
3. Navigate to the genesys-to-lex-automated-chatbot-designer folder and run the next instructions:

   sam construct --use-container
   sam deploy –guided

The primary command builds the supply of your software. The second command packages and deploys your software to AWS, with a collection of prompts:

• Stack Identify – Enter the title of the stack to deploy to AWS CloudFormation. This ought to be distinctive to your account and Area; a very good place to begin is one thing matching your mission title.
• AWS Area – Enter the Area you wish to deploy your app to. Make sure that it’s deployed in the identical Area because the EventBridge occasion bus.
• Parameter GenesysBusname – Enter the bus title created while you configured the Genesys integration. The sample of the bus title ought to appear like aws.accomplice/genesys.com/*.
• Parameter ClientId – Enter the shopper ID you copied earlier.
• Parameter ClientSecret – Enter the shopper secret you copied earlier.
• Parameter FileNamePrefix – Change the default file title prefix for the goal transcript file within the uncooked S3 bucket or hold the default.
• Parameter GenCloudEnv – Enter is the cloud surroundings for the particular Genesys group. Genesys is accessible in additional than 15 Areas worldwide as of this writing, so this worth is obligatory and will level to the surroundings the place your group is created in Genesys (for instance, usw2.pure.cloud).
• Verify modifications earlier than deploy – If set to sure, any change units shall be proven to you earlier than deployment for handbook overview. If set to no, the AWS SAM CLI will routinely deploy software modifications.
• Enable SAM CLI IAM function creation – Many AWS SAM templates, together with this instance, create AWS Id and Entry Administration (IAM) roles required for the Lambda capabilities included to entry AWS companies. By default, these are scoped right down to the minimal required permissions. To deploy a CloudFormation stack that creates or modifies IAM roles, you could present the CAPABILITY_IAM worth for capabilities. If permission isn’t supplied by way of this immediate, to deploy this instance, you could explicitly go --capabilities CAPABILITY_IAM to the sam deploy command.
• Save arguments to samconfig.toml – If set to sure, your decisions shall be saved to a configuration file contained in the mission, in order that sooner or later you may rerun sam deploy with out parameters to deploy modifications to your software.

After you deploy your AWS SAM software in your account, you may take a look at that Genesys transcripts are being despatched to your account and being reworked into the required format for the Amazon Lex automated chatbot designer.

Make a take a look at name to validate the answer

After you could have arrange the Genesys EventBridge integration and deployed the previous AWS SAM template, you can also make take a look at calls and validate that information are ending up within the S3 bucket for reworked information. At a excessive stage, you’ll want to carry out the next steps:

1. Make a take a look at name to your Genesys occasion to create a transcript.
2. Wait a couple of minutes and test the TransformedTranscript bucket for the output.

Run the automated chatbot designer

After you could have a couple of days’ price of transcripts saved in Amazon S3, you may run the automated chatbot designer by way of the Amazon Lex console utilizing the steps on this part. For extra details about the minimal and most quantity of turns for the service, discuss with Put together transcripts.

1. On the Amazon Lex V2 console, select Bots within the navigation pane.
2. Select Create bot.
   
3. Choose Begin with transcripts because the creation technique.
4. Give the bot a reputation (for this instance, InsuranceBot) and supply an optionally available description.
5. Choose Create a task with fundamental Amazon Lex permissions and use this as your runtime function.
   
6. After you fill out the opposite fields, select Subsequent to proceed to the language configuration.
7. Select the language and voice on your interplay.
   
8. Specify the Amazon S3 location of the transcripts that the answer has transformed for you.
9. Add further native paths if in case you have a particular a folder construction inside your S3 bucket.
   
10. Apply a filter (date vary) on your enter transcripts.
    
11. Select Achieved.

You need to use the standing bar on the Amazon S3 console to trace the evaluation. Inside a couple of hours, the automated chatbot designer surfaces a chatbot design that features consumer intents, pattern phrases related to these intents, and an inventory of all the data required to satisfy them. The period of time it takes to finish coaching relies on a number of elements, together with the amount of transcripts and the complexity of the conversations. Sometimes, 600 strains of transcript are analyzed each minute.

12. Select Overview to view the intents and slot varieties found by the automated chatbot designer.
    

The Intents tab lists all of the intents together with pattern phrases and slots, and the Slot varieties tab offers an inventory of all of the slot varieties together with slot sort values.

13. Select any of the intents to overview the pattern utterances and slots. For instance, within the following screenshot, we select ChangePassword to view the utterances.
    
14. Select the Related transcripts tab to overview the conversations used to determine the intents.
    
15. After you overview the outcomes, choose the intents and slot varieties related to your use case and select Add.

This provides the chosen intents and slot varieties to the bot. Now you can iterate on this design by making modifications comparable to including prompts, merging intents or slot varieties, and renaming slots.

You could have now used the Amazon Lex automated chatbot designer to determine widespread intents, utterances mapped to these intents, and data that the chatbot wants to gather to satisfy sure enterprise capabilities.

Clear up

While you’re completed, clear up your assets through the use of the next command throughout the AWS SAM CLI:

Conclusion

This put up confirmed you the way to use the Genesys Cloud CX and EventBridge integration to ship your Genesys CX transcripts to your AWS account, rework them, and use them with the Amazon Lex automated chatbot designer to create pattern bots, intents, utterances, and slots. This structure can assist first-time AWS CCI customers and present AWS CCI customers onboard extra chatbots utilizing the Genesys CX and Amazon Lex integration, or in steady enchancment alternatives the place chances are you’ll wish to evaluate your present intent design to that outputted by the Amazon Lex automated chatbot designer. For extra details about different AWS CCI capabilities, see Contact Heart Intelligence.

In regards to the Authors

Joe Morotti is a Options Architect at Amazon Net Companies (AWS), serving to Enterprise prospects throughout the Midwest US. He has held a variety of technical roles and revel in displaying buyer’s artwork of the attainable. In his free time, he enjoys spending high quality time together with his household exploring new locations and over analyzing his sports activities crew’s efficiency.

Anand Bose is a Senior Options Architect at Amazon Net Companies, supporting ISV companions who construct enterprise functions on AWS. He’s enthusiastic about creating differentiated options that unlock prospects for cloud adoption. Anand lives in Dallas, Texas and enjoys travelling.

Teri Ferris is accountable for architecting nice buyer experiences alongside enterprise companions, leveraging Genesys expertise options that allow Expertise Orchestration for contact facilities. In her function she advises on resolution structure, integrations, IVR, routing, reporting analytics, self-service, AI, outbound, cell capabilities, omnichannel, social channels, digital, unified communications (UCaaS), and analytics and the way they’ll streamline the shopper expertise. Earlier than Genesys, she held senior management roles at Human Sources, Payroll, and Studying Administration corporations, together with overseeing the Contact Heart.