Observe.AI has introduced a new generative AI product suite that aims to supercharge agent productivity through the company's proprietary large language models (LLMs) trained on a domain-specific dataset of millions of customer interactions.
Because domain-specific LLMs are trained on real-life contact center data, companies can expect enhanced agent performance at all stages - before, during, and after customer interactions. Capabilities like real-time answers, automated call summarization, and automated coaching notes contribute to higher agent self-improvement.
“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. 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,” said Swapnil Jain, CEO and Co-Founder of Observe.AI.
Generic vs. Domain-Specific LLM
Generic LLM is said to hamper contact center effectiveness as they lack specificity and control, according to Observe.AI. What's more, these models cannot discern right from wrong responses, which can be detrimental to real-life interactions. Generic models also tend to provide serious inaccuracies and “hallucinations” in AI, posing harm to the overall business.
Observe.AI’s Contact Center LLM, on the other hand, is built on 5+ years of human calibration and feedback to deliver higher accuracy and control. It meets the unique needs of the contact center and targets the customers' specific business objectives and use cases.
“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,” said Vache Moroyan, SVP of Product at Observe.AI.
Observe.AI cites accuracy and flexibility as the major differentiators that draw customers to their platform. The new generative AI suite bring features like Knowledge AI, Auto Summary, and Auto Coaching, which largely improve agent performance at all stages of customer interaction.
While Knowledge AI saves agents the time they'd spend manually searching knowledge bases and FAQs, Auto Summary automatically and consistently captures interaction summaries in multiple formats. As soon as a customer interaction ends, Auto Coaching generates coaching notes for agents to propel immediate skills and performance improvement on top of regular supervisor coaching.
Back in January, Observe.AI launched Supervisor Assist as part of its real-time AI product suite to help contact center supervisors monitor live calls and prioritize interactions requiring assistance or intervention.
