Domain-Specific AI: Adapting Natural Language Processing Solutions for Retail, Banking, and Airlines

Subham Kumar

20 Jun 2020 — 3 min read

Gaurav Kashyap

In the context of artificial intelligence, Natural Language Processing (NLP) has emerged as a key to improving customer experience across industries. This is particularly evident in the retail, banking, and airline industries where NLP is transforming customer relations, improving performance, and spurring change.

As a top professional in this field, Gaurav Kashyap has been able to make a great impact in the enhancement of the domain-specific AI solutions especially by the continued development of the NATAS (Natural Language Assistant) NLP system. In this area, he has effectively implemented the NATAS system to handle multiple types of customer engagements.

Since the web application was designed to have an additional text/SMS interface, he expanded the applicability of this tool to include more users. This customisation was also applied to the development of domain-specific ontologies and pre-packaged canned query sets for sectors such as retail banking and finance that offered customers a more natural, targeted, and immediate query experience. These improvements not only shifted the focus of the tool to the customers but also contributed to the growth of the product to the level of enterprise visibility and engagement of clients from various industries, who were encouraged to test the system.

His contributions have benefited the organization in several ways. By frequently demonstrating the product as well as conducting pilot installations, his team managed to present the product to a specific user group, which greatly enhanced its recognition within the company and among its permanent stakeholders. After the production version was released, customers observed increased team effectiveness in their communication with business applications. This allowed the solution to save a significant amount of time for database queries, which improved their user experience.

It helped not only satisfy customers but also put the product into a category that other industries would value. The modification of the NATAS system interface and its dataset for individual clients was a significant project. This was a process of working closely with customer business analyst and architects to fine-tune the solution for customers. It also required constant evaluation and multiple versions of the product to be integrated into live business settings. The onboarding of these clients was a success in that it proved the viability and applicability of the solution in different industries.

The benefits of such projects can also be assessed by the extent of awareness and adoption in the various sectors. The organization became widely known not only due to the NLP tool but also because of other research works, such as AI enhancements for face detection. This visibility led to several new projects being signed on and helped cement the tool as the market leader in its category.

However, transferring a research-based product to a business environment was difficult. In the beginning, the product only provided a small amount of data; it was then decided that further enhancements had to be made to feed the customer’s data requirements. Furthermore, the customer’s application database was very large, and the IT infrastructure from the server side to the business rule engine had to be redesigned. These enhancements made it possible to process big data and produce accurate ontologies relevant to a specific domain for the best performance.

Through his work, “AI For Information Retrieval: Advancements in Search Engines and Chatbots through Deep Learning-Based Query Understanding”, Gaurav Kashyap has highlighted recent advancements in deep learning and natural language processing that have revolutionized information retrieval, enhancing search engines and chatbots to better understand and respond to user queries.

As suggested by industry experts like him, even more potential for the future of NLP in customer service can be observed further on. The current evolution towards automation in customer support using chatbots and virtual assistants is changing the nature of business-consumer relationships. With the help of NLP, understanding customers’ intentions, and providing the appropriate response to them, customer service has become faster, more efficien,t and more personalized.

In conclusion, the industry is experiencing the growth of multimodal conversational interfaces, which use both text and voice for effective communication. This trend coupled with improvements in emotional intelligence and speech recognition will only further entrench NLP in the development of better and user-centric AI systems.