Observe.AI Debuts Contact Center LLM With 30 Billion Parameters and Generative AI Tools
Contact center AI platform Observe.AI has created a 30-billion-parameter large language model (LLM) and introduced a set of generative AI tools built on the proprietary model for its customers. The new Contact Center LLM is trained on customer service interactions and provides a foundation for the accompanying generative AI tools that Observe claims will better assist contact center agents than those built on more generic LLMs.
Generative Observe
Enterprise generative AI products have mushroomed as LLM developers like OpenAI and Google have pitched businesses on how LLMs can aid businesses. Observe.ai cites a Gartner study showing 70% of businesses are exploring using generative AI in some form. Usually, that involves fine-tuning the model or augmenting its training with a company’s data. The LLM starts with training on domain-specific datasets made from customer interactions. The hundreds of millions of conversations used in training are redacted to remove personal or private information. The Contact Center LLM’s focus on industry data reduces the hallucinations and wrong answers that sometimes plague ChatGPT and other generative AI services, according to Observe.ai. In tests against OpenAI’s GPT-3.5, the basis for ChatGPT, the Contact Center LLM had a 35% higher accuracy for the contact center LLM when summarizing conversations and a 33% higher accuracy for sentiment analysis.
Observe.ai claims the contact center data focus is a key element to making the Generative AI Suite built on the LLM better than the tools built by models that may be bigger or have larger training databases. There are three generative AI tools in Observe.ai’s initial suite. They include an internal conversational generative AI search engine for contact center employees, the summarization of customer interactions, and the automated generation of suggestions and coaching notes right after a call ends.
“We’re at an exciting precipice for the use of generative AI in contact centers – an inflection point on par with the advent of the cloud or mobile,” Observe.ai CEO Swapnil Jain said. “It’s a critical moment that will separate the disruptors from the disrupted, and contact centers who move forward with LLM strategies based on accuracy, calibration, and control will realize their fullest potential.”
Enterprising AI
Observe.ai has plenty of competition in both LLM and generative AI products for customer service. For instance, customer service automation startups like Ada, NLX, Hyro, and Conversica have embedded generative AI into their platforms, while LivePerson and Cohere have partnered to bring custom LLMs to enterprise services. Big names like Salesforce are eager to be part of the generative AI enterprise explosion. as have Yellow.ai with its Yellow.ai and Gupshup. Observe.ai proves the shape of the market is far from settled.
“By leveraging a domain-specific LLM, we’re able to drive deeper trend analysis, more accurate call summarization, and in-context question answering while ensuring degrees of control, calibration, and privacy that are simply not possible with generic models,” Observe.ai senior vice president of product Vache Moroyan said.
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