This Guidance uses Artificial Intelligence (AI) to analyze speech and conversations in near real-time to improve agent key performance indicators (KPIs) and the customer experience. Data can be visualized through dashboards on Amazon QuickSight, increasing the visibility into agent effectiveness and improving the customer experience. Machine learning is used to capture intent and context from conversations and offer intelligent search features.

Architecture Diagram

Download the architecture diagram PDF 

Well-Architected Pillars

The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

  • Data, such as sentiment analysis of speakers and how well contact center agents meet a customer’s internal compliance rules, is used to identify how effective contact center agents are at handling customer calls. The same data also identifies the topics and entities discussed in the call. All of this data can be visualized in Amazon QuickSight to help business analysts identify trends from a customer’s perspective and potential training needs for agents. 

    Read the Operational Excellence whitepaper 

  • All data is encrypted both in motion and at rest, and can use customer-controlled AWS Key Management Service (AWS KMS) keys for this encryption. Although the solution is entirely serverless, the AWS Lambda components can optionally run within a customer’s VPC, accessing external services such as Amazon Transcribe and Amazon S3 only through a customer’s approved endpoints.

    Read the Security whitepaper 

  • The solution is entirely serverless, and each of those services (for example, Amazon Transcribe, Amazon S3) operate using multiple Availability Zones in a resilient fashion.

    Read the Reliability whitepaper 

  • The solution scales its usage of its serverless components as it needs to, both up and down, in order to handle the concurrent processing of potentially thousands of calls or those times when there are no pending calls to process.

    Read the Performance Efficiency whitepaper 

  • As in the Performance Efficiency pillar, the solution will only use serverless components when there is an active call audio file to process, minimizing the incurred costs as much as possible. If required, the original audio files can be archived to lower-cost long-term storage on a customer-specified schedule in order to minimize storage costs. 

    Read the Cost Optimization whitepaper 

  • By extensively using managed services and dynamic scaling, we minimize the environmental impact of the backend services.

    Read the Sustainability whitepaper 

Implementation Resources

The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.

Open sample code on GitHub

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This post demonstrates how to use Amazon Machine Learning (ML) services to transcribe and extract insights from your contact center calls at scale. 

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.

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