Aren’t we living in the era of automation and machines?
YES! Agreeing with the fact that everything is made simpler and automatic, from day-to-day activities to our entertainment search with voice recognition. Industries and enterprises are looking for automation of processes that reduce human efforts in repetitive tasks.
Tech people were in search of automation of tasks with human intelligence. Artificial intelligence popped as a solution for automation that mimics human brains. AI has now outperformed in various areas of learning the issues and resolving the bugs. One such realm is AI-based customer service application!
AI is becoming a real game-changer in the conversational customer support services of businesses. NewVoiceMedia has reported that US enterprises are losing around $75 billion per year due to poor customer service. There is a positive impact on adopting AI solutions for businesses in various areas. Financial services have already adopted the conversational AI customer service application to a greater extent.
In this blog post, let’s see how we created an integrated QA model for AI-based customer service application.
As of now, there is no modern AI implemented in sectors. Narrow AI is hitting hard in many industries and is applied in a wide range of data training models and machine learning concepts. Many businesses have adopted AI in various applications such as chatbots, eCommerce, healthcare, customer service, human resource management, crypto trading, crypto mining, workplace communication, and much more.
Soon, the most advanced AI would be monitoring patient records with IoT sensors and prevent malicious health issues in the upcoming era! However, there is a big challenge in testing AI-based applications.
AI automatically collects the data and mimics human intelligence to process further. Hence, it’s hard to build an automated testing solution to detect the behavior of AI applications. You need the help of technical experts who can understand the AI application behavior and automate the testing suite.
Now, let’s get into our client’s business challenges on AI-based customer service applications!
Our Client served as a leading digital customer experience platform rendering solutions for enterprises with conversational service automation.
They develop customer service solutions using speech recognition, voice assistants, and voice biometrics from devices. Their solutions automate customer service agent interactions, real-time conversation analytics, after-call workarounds, and customer response management.
The conversational customer service application is compatible with deploying in on-premises and cloud environments as per the customer requirements.
Our client solution provides voice and data solutions with speech recognition and voice biometrics to host automated customer service applications. The application is built with the tech stack of Angular and Java, which is available on the cloud platform.
The AI-based customer service application required an external QA partner with efficient strategies. Indium being a thought leader in Quality Assurance helped validate, capture the full-functional integrity and response to usability. In every software development cycle, the client required quality assurance to ensure efficient customer experience.
Now, let’s look at our client faced business challenges and the solutions offered by our team!
Our client was continuously going through the software development cycle process of development, testing, and maintenance to enhance the customer experience. In every phase, their in-house QA team was struggling to automate the test suite and optimize the test coverage.
Indium offered automated testing solutions with regression testing and wide test coverage with associated workflows, conversation triggers.
Now, let’s dive in to know about the client’s QA challenges and our solutions in detail!
The in-house QA team had to go through manual or smoke testing after every patch development to ensure high-level quality checks in the application. AI-based customer service application collects data and updates their services. Hence, they were in the push to schedule monthly patches and releases to continuously support quality assurance.
Indium rendered unit testing solutions powered by DevOps code and adoptive shift learning. These solutions established standards in the codebase and accelerated the development cycle.
We also built the hyper-iterative test design specially designed for AI-based applications. These solutions upgrades were on-demand with AI learning, features, knowledge, and test cases.
The QA team built a rigid Java-Selenium-based framework for testing the application suite. But, it was not compatible with automating UI-based testing. The in-house team was not ready with automation or test cycle optimization.
We built a test design to execute various input-output combinations for the business requirements and validations of the application’s reliability to error handling scenarios. The test strategy also covered test cases for potential response flows and usability parameters. A test automation platform- uphoriX was developed on open-source frameworks, custom libraries integrated with CI/CD(Azure), and Protractor for supporting angular-based application testing.
Our team also offered an on-going test suite with an automated setup with scheduled iterations for updating test cases, test coverages, and AI learning pace. We efficiently automated 1700+ regression cases that contributed to 80% of test execution efficiency.
The QA team did not address the test case volume, traceability for testing the comprehensive suite of projects. They did not have regular QA reports or fixes, and also, there were no proper alerts for stakeholders.
Our team rendered a multi-dimensional testing suite that covered multichannel and cross-channel customer experience, domain validations, contextual test cases involving conversational application. We generated QA reports and alerts to be updated through email and SMS for specified stakeholders.
Our client was hosting the AI-based customer service application on the cloud, and hence they requested cloud deployment. We built a test suite that is compatible with on-prem/cloud deployment.
Our client was happier with reducing time and efforts employed in regression testing and report generation to a greater extent. The automation test suite helped the in-house QA team focus on complex real-time glitches rather than answering regression cases with customer service.
Now, wrapping up with the highlights of the business impact created by our automated test suite!
By Uma Raj
By Uma Raj
By Abishek Balakumar