Building a next generation AI platform
How CA Jarvis is using robotics to enhance UX, simplify human tasks
Everyone is unique. Creating outstanding customer experiences means understanding these differences, and providing personalized connections to build trust and develop positive relationships.
While many elements of the customer experience are already automated, nowadays companies are taking it a step further. Recent advances in Big Data have enabled the processing of tremendous amount data, needed to personalize services that precisely target customer needs. Thus, many businesses are exploring how to use artificial intelligence (AI) and machine learning (ML) to generate a whole new type of experience – one that involves adaptive, self-learning robots.
Is this the right time to use robots for personalized customer service, to minimize low-value and repetitive tasks? Possibly. The answer lies in whether Big Data, AI and ML can create memorable customer experiences.
Leo’s service robot, called Bill-B1 ( high 55 inch, weight 97 pound), comes equipped with many useful technologies, such as infrared credit card readers, an HD camera, LED eyes, a thermal printer, LCD screen, laser ranging radar and even crash sensors. Bill-B1 can speak with customers in multiple languages, engage them with basic questions, read bank cards, perform facial recognition and even manage data queueing. These attributes make Bill-B1 a quite useful store assistant. But is he capable of more?
In a large banking corporation, new promotions arise almost every day. The business office managers are normally tasked with making recommendations to customers based on their preferences and qualifications. However, without comprehensively analyzing each customer profile, it’s hard to give personalized recommendations to customers. Could Bill-B1 do a better job?
The short answer, is yes.
When engaging with exsisting customers, Bill-B1 is able to read their banking cards and access their user profiles. The conversations between customers are streamed into Jarvis and then useful information can be processed by Cloud based NLP services. Then multiple pre-trained models ( including neural networks and ensemble learning based algorithms ) that have been triggered to generate recommedations to users. The prediction results are further constructured to robots’s language for response. Also, CA Jarvis’ dashboard empowers Bill-B1 to present useful insights, such as the estimated customer wait time, hourly store traffic volume, exchange rates and investment trends… not to mention the personalized promotion recommendations.
In launching this project, we have learned that the major effort in creating a successful data science application is not just optimizing the models but also putting these models into production. Choosing the right AI platform is a critical and ubiquitous for not only data scientists, data engineers and architects, but also for operation teams and project managers. The next generation of AI platforms should be able to handle various types of data flows, support both real-time and batch-mode applications, manage entire model life cycles, as well as elastically scale run-time resources on demand of data and applications.
Specifically, a successful AI platform should:
The CA Jarvis team is working to address all the challenges inherent in creating a top-notch AI platform. We are excited for upcoming enhancements, such as Microservices JAF, which will provide flexible architecture to support various deep learning frameworks; the integration of Jupyter notebooks with JAF, which will allow data scientists to operate data and models directly with Jarvis, enabling model evaluations and performance metrics in both notebooks and CA Jarvis Dashboard.
Meanwhile, Leo Robot continues to work with banks to advance their exploration of robot-led banking services. They are also exploring new business lines from health care, to retail, transportation and tourism, etc. Our dream is that one day, the next generation of CA Jarvis will power Leo Robot to revolutionize the customer experience.