ChatGPT has remained atop technology headlines for months, but it still lacks the accuracy for many customer-facing applications. With an 85% accuracy rate, large language models (LLMs) are a good indication of the potential of this technology in solutions designed to enhance the customer experience, but in their current (raw) states they lack the dependency for integration directly into a bank or insurance firm's systems, for example.
Some companies are resolving this issue by integrating the predictive capabilities of LLMs with the enterprise-grade control they have achieved through their conversational AI platforms. One such organization is conversational AI company Boost.ai, which last week introduced Version 12 of its platform, the first of a series of planned updates by the company to incorporate LLM-enriched features. This iteration is focused on key customer experience (CX) improvements, including content suggestion, content rewriting and accelerated generation of training data.
Boost.ai noted that the new update will take advantage of generative AI technology to suggest messaging content to AI trainers within the Boost.ai platform, generating suggested responses and resulting in drastically reduced implementation times for new intents. The company said it remains committed to researching, developing, releasing, and maintaining responsible implementations of LLM-powered, enterprise-quality conversational AI features in order to further enhance the customer experience.
In integrating LLM technology with its platform, Boost.ai has created a hybrid natural language understanding (NLU) system that offers more accuracy, flexibility and cost-effectiveness. Expertly weaving together the right AI components for each task, Boost.ai’s platform ensures precise and accurate answers - "harnessing the benefits of LLMs while still adhering to stringent quality assurance requirements," said the company.
“LLM technology offers great promise, but most applications just aren’t properly designed to securely and scalably support real-world businesses. With worries about accuracy or even inappropriate behavior, established institutions like banks could not risk direct access to this iteration of generative AI - until now,” said Jerry Haywood, CEO of Boost.ai. “By pairing LLMs with our conversational AI, we’re able to ensure accuracy and open the door for customers in sensitive industries like financial services. We’re proud to be pioneering a way forward for businesses to harness this tech right now. It’s available for customers to use and enhance their existing solution, and to help them achieve speed to value significantly sooner whilst minimizing the risks currently dominating headlines.”
Edited by
Alex Passett
