Observe.AI has launched a Generative AI Suite powered by a contact center large language model (LLM) to enhance agent performance.
That new 30-billion-parameter Contact Center LLM is trained on a dataset of vast quantities of customer interactions.
The Generative AI Suite leverages this LLM to help agents by surfacing real-time answers, mechanizing call summarization, and automating coaching notes for agents.
Swapnil Jain, CEO and Co-Founder of Observe.AI, said: “We’re at an exciting precipice for the use of generative AI in contact centres – 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 centres who move forward with LLM strategies based on accuracy, calibration, and control will realize their fullest potential.
A New Breed of Contact Center
Observe.AI claims its Contact Center LLM provides more accuracy and control as a result of over five years of feedback and adjustments.
As such, the model fits with the unique needs of contact centers, offering higher performance levels than generic models.
Moreover, contact centers can further tailor it to the needs of specific business objectives.
According to Observe.AI, the Contact Center LLM is 35 percent more accurate than GPT3.5 in creating automatic conversation summaries and 33 percent more accurate in sentiment analysis. These figures should grow further, with more training.
Moreover, the vendor ensures customer data privacy by applying “the industry’s most accurate” redaction techniques, removing any Personally Identifiable Information (PII).
As Vache Moroyan, SVP of Product at Observe.AI, commented:
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.
More on the Generative AI Suite
Alongside the aforementioned capabilities, Oberserve.AI’s Knowledge AI comes with the suite.
With this, agents no longer need to spend time manually searching knowledge bases and FAQs to find the answers to customer questions.
As a result, Oberserve.AI claims first call resolution increases and average handling times drop.
Also, the suite captures auto-summaries of interactions in multiple formats, including structured, unstructured, and entities, which takes away the need for after call work (ACW).
This allows agents to focus more on other areas relating to the customer, as well as improving the quality of notes.
Finally, auto coaching provides agents with automatically generated coaching notes as soon as a customer interaction finishes.
According to the vendor, this AI coaching feature enhances agents’ skills, bolsters customer experiences, and leads to quicker performance feedback alongside supervisor-assisted coaching.
In April this year, Observe.AI launched real-time agent and supervisor assistance tools.
Through these, it aims to address the typical customer interaction challenges faced by agents using a combination of performance data and historical insights.
Last year, Observe.AI added Reporting & Analytics, which are new capabilities that give an overview of conversation intelligence and contact center performance.
It includes interactive visualizations with insights on key metrics, such as positive and negative customer experience drivers, customer sentiment, and coaching and revenue opportunities.
