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The rise of synthetic intelligence (AI) has created alternatives to enhance the client expertise within the contact middle house. Machine studying (ML) applied sciences regularly 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 will help you obtain decreased wait occasions, clever routing, decreased time to decision by means of self-service features or information assortment, and improved internet 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 gathering the required info, which it palms over to an agent for processing.
With Amazon Lex bots, you need to use conversational AI capabilities to allow these capabilities inside your name middle. Amazon Lex makes use of automated speech recognition (ASR) and pure language understanding (NLU) to grasp the client’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 Middle Intelligence (AWS CCI) to rework the trendy 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 information assortment capabilities.
When exploring AWS CCI capabilities with Amazon Lex and Genesys Cloud, it’s possible you’ll be not sure of the place to begin 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 that you may then refine and launch conversational experiences quicker based mostly in your present name transcripts. With the automated chatbot designer, Amazon Lex clients and companions have an easy and intuitive means of designing chatbots and may 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 mechanically 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:
Genesys Cloud sends iterative transcripts occasions to your EventBridge occasion bus.
Lambda receives the iterative transcripts from EventBridge, determines when a dialog is full, and invokes the Transcript API inside Genesys Cloud and drops the complete transcript in an S3 bucket.
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.
The Amazon Lex automated chatbot designer makes use of ML to construct an preliminary bot design based mostly on the offered Genesys Cloud transcripts.
Stipulations
Earlier than you deploy the answer, you will need to full the next stipulations:
Arrange your Genesys Cloud CX account and guarantee that you’ll be able to log in. For extra info on establishing your account, confer with the Genesys documentation.
Be sure that the proper permissions are set for enabling and publishing transcripts from Genesys. For extra info on establishing the required permissions, confer with Roles and permissions overview.
If PCI and PII encryption is required for transcription, ensure that it’s arrange in Genesys. For extra info on establishing the required permissions, confer with Are interplay transcripts encrypted when saved within the cloud.
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:
Log in to the Genesys Cloud surroundings.
Select Admin, Integrations, Add Integrations, and Amazon EventBridge Supply.
On the Configuration tab, present the next info:
For AWS Account ID, enter your AWS account ID.
For AWS Account Area, enter the Area the place you need EventBridge to be arrange.
For Occasion Supply Suffix, enter a suffix (for instance, genesys-eb-poc-demo).
Save your configuration.
On the EventBridge console, select Integration within the navigation pane, then select Associate occasion sources.
There needs to be an occasion supply listed with a reputation like aws.accomplice/genesys.com/…/genesys-eb-poc-demo.
Choose the accomplice occasion supply and select Affiliate with occasion bus.
The standing modifications from Pending to Lively. 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.
Navigate to the Genesys Cloud occasion.
Select Admin, Integrations, and OAuth.
Select Add Consumer.
On the Consumer Particulars tab, present the next info:
For App Title, enter a reputation (for instance, TranscriptInvoke-creds).
For Grant Sorts, choose Consumer Credentials.
Ensure you’re utilizing the proper function that has entry to invoke the Transcribe APIs.
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 may have arrange the Genesys EventBridge integration, you possibly can 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:
Set up AWS SAM if not put in already. For directions, confer with Putting in the AWS SAM CLI.
Obtain the GitHub repo and unzip to your listing.
Navigate to the genesys-to-lex-automated-chatbot-designer folder and run the next instructions:
The primary command builds the supply of your utility. The second command packages and deploys your utility to AWS, with a collection of prompts:
Stack Title – Enter the identify of the stack to deploy to AWS CloudFormation. This needs to be distinctive to your account and Area; an excellent place to begin is one thing matching your challenge identify.
AWS Area – Enter the Area you wish to deploy your app to. Be sure it’s deployed in the identical Area because the EventBridge occasion bus.
Parameter GenesysBusname – Enter the bus identify created if you configured the Genesys integration. The sample of the bus identify ought to seem like aws.accomplice/genesys.com/*.
Parameter ClientId – Enter the consumer ID you copied earlier.
Parameter ClientSecret – Enter the consumer secret you copied earlier.
Parameter FileNamePrefix – Change the default file identify prefix for the goal transcript file within the uncooked S3 bucket or preserve the default.
Parameter GenCloudEnv – Enter is the cloud surroundings for the precise Genesys group. Genesys is accessible in additional than 15 Areas worldwide as of this writing, so this worth is necessary 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 guide overview. If set to no, the AWS SAM CLI will mechanically deploy utility 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 features included to entry AWS providers. By default, these are scoped all the way down to the minimal required permissions. To deploy a CloudFormation stack that creates or modifies IAM roles, you will need to present the CAPABILITY_IAM worth for capabilities. If permission isn’t offered by means of this immediate, to deploy this instance, you will need to 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 challenge, in order that sooner or later you possibly can rerun sam deploy with out parameters to deploy modifications to your utility.
After you deploy your AWS SAM utility in your account, you possibly can 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 may have arrange the Genesys EventBridge integration and deployed the previous AWS SAM template, you may make check calls and validate that information are ending up within the S3 bucket for remodeled information. At a excessive degree, you must carry out the next steps:
Make a check name to your Genesys occasion to create a transcript.
Wait a couple of minutes and test the TransformedTranscript bucket for the output.
Run the automated chatbot designer
After you may have just a few days’ value of transcripts saved in Amazon S3, you possibly can run the automated chatbot designer by means 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, confer with Put together transcripts.
On the Amazon Lex V2 console, select Bots within the navigation pane.
Select Create bot.
Choose Begin with transcripts because the creation technique.
Give the bot a reputation (for this instance, InsuranceBot) and supply an elective description.
Choose Create a job with primary Amazon Lex permissions and use this as your runtime function.
After you fill out the opposite fields, select Subsequent to proceed to the language configuration.
Select the language and voice in your interplay.
Specify the Amazon S3 location of the transcripts that the answer has transformed for you.
Add extra native paths when you have a selected a folder construction inside your S3 bucket.
Apply a filter (date vary) in your enter transcripts.
Select Carried out.
You should use the standing bar on the Amazon S3 console to trace the evaluation. Inside just a few hours, the automated chatbot designer surfaces a chatbot design that features person intents, pattern phrases related to these intents, and an inventory of all the knowledge required to meet them. The period of time it takes to finish coaching is dependent upon a number of elements, together with the amount of transcripts and the complexity of the conversations. Usually, 600 strains of transcript are analyzed each minute.
Select Evaluate 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.
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.
Select the Related transcripts tab to overview the conversations used to determine the intents.
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 akin to including prompts, merging intents or slot sorts, and renaming slots.
You’ve now used the Amazon Lex automated chatbot designer to determine widespread intents, utterances mapped to these intents, and knowledge that the chatbot wants to gather to meet sure enterprise features.
Clear up
Once you’re completed, clear up your assets through the use of the next command inside 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 will 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 it’s possible 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 Middle Intelligence.
Concerning the Authors
Joe Morotti is a Options Architect at Amazon Net Providers (AWS), serving to Enterprise clients throughout the Midwest US. He has held a variety of technical roles and revel in exhibiting buyer’s artwork of the attainable. In his free time, he enjoys spending high quality time along with his household exploring new locations and over analyzing his sports activities group’s efficiency.
Anand Bose is a Senior Options Architect at Amazon Net Providers, supporting ISV companions who construct enterprise functions on AWS. He’s enthusiastic about creating differentiated options that unlock clients 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 resolution structure, integrations, IVR, routing, reporting analytics, self-service, AI, outbound, cellular capabilities, omnichannel, social channels, digital, unified communications (UCaaS), and analytics and the way they will streamline the client expertise. Earlier than Genesys, she held senior management roles at Human Assets, Payroll, and Studying Administration corporations, together with overseeing the Contact Middle.
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