How GenAI and Real-Time Data Products Will Revolutionize Customer Experience

Imagine a mobile phone operator’s chatbot engaging with a client on the operator’s website. The client reports that her internet is down, and the chatbot responds with generic step-by-step instructions to check the router, cables, and connections. 

Now imagine a different scenario. As soon as the client starts the interaction, the chatbot accesses real-time customer data from the underlying information systems, including network data. It learns that there’s a hardware failure affecting all consumers in the neighborhood, and immediately offers an ETA and an SMS notification for service restoration.

This might seem like science fiction, but it’s possible with real-time data products, retrieval-augmented generation (RAG), and generative AI (GenAI) technologies.

Understanding Data Products

A data product is a reusable data asset that bundles data with everything needed to make it independently usable by authorized consumers. Data products can power AI-driven software apps that act on insights derived from the data.

Data products that integrate, process, and translate real-time customer data into large language models (LLMs) can enable GenAI to deliver real-time personalization.

Introducing Retrieval-Augmented Generation (RAG)

LLMs are neural networks that are trained on large, historical datasets to generate natural language responses to various prompts. However, they do not have access to the latest information on a topic, so their responses can be inaccurate or irrelevant “hallucinations”.

Retrieval-augmented generation (RAG) is a design pattern that allows LLMs to connect with contextual and current data (from databases and document stores) and generate more relevant and accurate responses, increasing their trustworthiness.

By retrieving real-time customer data and translating it into intelligent prompts for the LLM, RAG can provide personalized customer service.

How Data Products and RAG Work Together

A data product can be invoked by the chatbot to retrieve and integrate the necessary customer data from the enterprise data sources, translate it into a relevant prompt, and feed it to the LLM along with the client’s inquiry. The LLM then generates an accurate and personalized response for the user.

The data product employs various methods of accessing customer data in real-time systems: APIs (if these exist), streaming, messaging, or CDC – or any combination of these methods to integrate data from disparate data sources.

Combining real-time data products and RAG is useful for various use cases, such as generating hyper-personalized marketing campaigns, accelerating the resolution of technical support and field service issues, generating personalized cross-sell and up-sell recommendations for call center agents, and more.

The Future of Data Products and RAG

As technology evolves, we can expect to see rapid advances in data products and RAG, and an expanding list of use cases that they can power.

Advances will likely include data products that can process larger volumes of data in milliseconds, handle both tabular data and unstructured documents, and generate prompts that are optimized for the industry and specific use case.

Data products and RAG technology are here. They promise to elevate customer experience to new levels, making them a game-changer for business-to-consumer enterprises.