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 middle area. Machine studying (ML) applied sciences frequently enhance and energy the contact middle buyer expertise by offering options for capabilities like self-service bots, reside name analytics, and post-call analytics. Self-service bots built-in together with your name middle may also help you obtain decreased wait instances, clever routing, decreased time to decision by self-service features or knowledge 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 info, which it fingers over to an agent for processing.

With Amazon Lex bots, you should utilize conversational AI capabilities to allow these capabilities inside your name middle. 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) supplies a contact middle platform in a public cloud mannequin that permits fast and easy integration of AWS Contact Center Intelligence (AWS CCI) to remodel the fashionable contact middle from a price middle right into a revenue middle. As a part of AWS CCI, Genesys Cloud integrates with Amazon Lex, which permits self-service, clever routing, and knowledge assortment capabilities.

When exploring AWS CCI capabilities with Amazon Lex and Genesys Cloud, chances are you’ll be not sure of the place to start out in your bot design journey. To help those that could also be beginning with a clean canvas, Amazon Lex supplies the Amazon Lex automated chatbot designer. The automated chatbot designer makes use of ML to offer an preliminary bot design which you could then refine and launch conversational experiences quicker primarily based 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 may cut back bot design time from weeks to hours. Nonetheless, the automated chatbot designer requires transcripts to be in a sure format that’s not aligned to Genesys Cloud transcript exports.

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

Answer 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 primarily based on the offered Genesys Cloud transcripts.

Stipulations

Earlier than you deploy the answer, you have to full the next conditions:

1. Arrange your Genesys Cloud CX account and make it possible for you’ll be able to log in. For extra info on establishing your account, check with the Genesys documentation.
2. Guarantee that the best permissions are set for enabling and publishing transcripts from Genesys. For extra info on establishing the required permissions, check with Roles and permissions overview.
3. If PCI and PII encryption is required for transcription, be certain it’s arrange in Genesys. For extra info on establishing the required permissions, check 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 info:
   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 Consumer.
10. On the Consumer Particulars tab, present the next info:
    1. For App Identify, enter a reputation (for instance, TranscriptInvoke-creds).
    2. For Grant Varieties, choose Consumer Credentials.

Be sure you’re utilizing the best function that has entry to invoke the Transcribe APIs.

11. Select Save.

This generates new values for Consumer ID and Consumer 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 have got arrange the Genesys EventBridge integration, you may deploy an AWS Serverless Application Model (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, check with Installing 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 sequence 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 superb place to begin is one thing matching your challenge title.
• AWS Area – Enter the Area you need to deploy your app to. Be certain that it’s deployed in the identical Area because the EventBridge occasion bus.
• Parameter GenesysBusname – Enter the bus title created once you configured the Genesys integration. The sample of the bus title ought to appear to be 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 maintain the default.
• Parameter GenCloudEnv – Enter is the cloud atmosphere for the precise Genesys group. Genesys is on the market in additional than 15 Areas worldwide as of this writing, so this worth is obligatory and will level to the atmosphere the place your group is created in Genesys (for instance, usw2.pure.cloud).
• Affirm modifications earlier than deploy – If set to sure, any change units will probably be proven to you earlier than deployment for handbook overview. If set to no, the AWS SAM CLI will robotically deploy software modifications.
• Enable SAM CLI IAM function creation – Many AWS SAM templates, together with this instance, create AWS Identity and Access Management (IAM) roles required for the Lambda features 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 have to present the CAPABILITY_IAM worth for capabilities. If permission isn’t offered by this immediate, to deploy this instance, you have to explicitly go --capabilities CAPABILITY_IAM to the sam deploy command.
• Save arguments to samconfig.toml – If set to sure, your decisions will probably be saved to a configuration file contained in the challenge, 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 check that Genesys transcripts are being despatched to your account and being remodeled into the required format for the Amazon Lex automated chatbot designer.

Make a check name to validate the answer

After you have got arrange the Genesys EventBridge integration and deployed the previous AWS SAM template, you may make check calls and validate that recordsdata are ending up within the S3 bucket for remodeled recordsdata. At a excessive stage, that you must carry out the next steps:

1. Make a check 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 have got a couple of days’ price of transcripts saved in Amazon S3, you may run the automated chatbot designer by the Amazon Lex console utilizing the steps on this part. For extra details about the minimal and most quantity of turns for the service, check with Prepare 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 methodology.
4. Give the bot a reputation (for this instance, InsuranceBot) and supply an non-obligatory description.
5. Choose Create a task with primary 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 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 Completed.

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 is determined by a number of components, together with the amount of transcripts and the complexity of the conversations. Usually, 600 traces of transcript are analyzed each minute.

12. Select Evaluation to view the intents and slot sorts found by the automated chatbot designer.
    

The Intents tab lists all of the intents together with pattern phrases and slots, and the Slot sorts tab supplies an inventory of all of the slot sorts together with slot kind 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 establish the intents.
    
15. After you overview the outcomes, choose the intents and slot sorts related to your use case and select Add.

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

You have got now used the Amazon Lex automated chatbot designer to establish frequent intents, utterances mapped to these intents, and data that the chatbot wants to gather to satisfy sure enterprise features.

Clear up

Whenever you’re completed, clear up your assets by utilizing the next command inside the AWS SAM CLI:

Conclusion

This submit confirmed you how you can 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 may also help 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 need to examine your present intent design to that outputted by the Amazon Lex automated chatbot designer. For extra details about different AWS CCI capabilities, see Contact Center Intelligence.

In regards to the Authors

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

Anand Bose is a Senior Options Architect at Amazon Net Providers, supporting ISV companions who construct enterprise purposes 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 answerable for architecting nice buyer experiences alongside enterprise companions, leveraging Genesys know-how options that allow Expertise Orchestration for contact facilities. In her function she advises on answer 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 Assets, Payroll, and Studying Administration firms, together with overseeing the Contact Heart.