Fin, formerly Intercom, has announced the launch of Fin Voice 2, its next-generation AI phone support agent designed for high-resolution customer service interactions.
Built with Apex Flash, this update delivers a reported 24.5% improvement in resolution rates, responding roughly half a second faster, and is optimized for natural conversations.
This launch highlights Fin’s strategy to build specialized AI for autonomous and scalable customer service outcomes.
Announcing the launch on LinkedIn, Eoghan McCabe, CEO and founder at Fin, argues that voice AI technology has now reached a level where it can reliably deliver the quality needed for mainstream adoption.
“Voice is just extremely hard,” he explained.
“And while we all know that the future of customer experiences will be agent-driven voice, we’re not there yet today. That changes today.”
When Conversation Isn’t Enough
As a general-purpose model, the original Fin Voice had been proven effective for conversation but was less optimized for the capabilities required for modern customer support.
Traditionally, early voice AI systems were often judged on their ability to hold natural conversations; however, many businesses are now turning their focus towards operational efficiency.
Whilst a general-purpose model can be highly capable across many domains, customer support now requires a narrower set of skills, and broad models can introduce unwanted variability in a support environment.
Today’s customers are less impressed by conversational novelty and now ask whether it can reliably solve customer problems, creating demand for systems optimized for support performance.
Built for Instant Response
The latest version of Fin’s AI-powered phone support agent is built on Apex Flash, a proprietary model developed for customer service voice interactions.
By replacing the general-purpose model, Fin has enabled the agent to move toward specialized support outcomes rather than broad conversational capabilities.
In fact, the change has delivered a 24.5% improvement in resolution rates and reduced response latency by roughly half a second, designed to better understand support requests and generate responses that sound more natural.
Compared to the original model, Fin Voice 2 is designed to resolve issues end-to-end by connecting directly to business systems and complete tasks without requiring a channel or agent transfer.
The launch also enables businesses to deliver more on-brand experiences without requiring the additional efforts associated with large call centers, allowing enterprise customers to maintain service standards across interactions.
Furthermore, Fin has introduced enhanced operational visibility through real-time insights and analysis of unresolved conversations, creating a continuous feedback loop for improved support quality.
Ultimately, these changes position Fin Voice 2 as a more autonomous and operationally focused customer service platform rather than simply a conversational AI tool.
Scaling Without Compromise
For CX teams, Fin Voice 2 is designed to improve both CX and the operational side of phone support, as latency reduction means customers receive responses more quickly.
This delay reduction helps customer conversations feel more fluid, reducing the pauses that can make voice AI interactions feel artificial.
Combined with higher resolution rates, this can help reduce customer effort and increase the likelihood that issues are resolved during the first interaction.
For customers, this update offers speed and convenience, having their issues addressed in a single conversation through faster responses and more natural dialogue, making AI-powered support feel less transactional and easier to engage with.
For support teams, higher routine automation offers a reduced workload to redirect focus to complex or sensitive cases.
Furthermore, real-time insights and unresolved conversation analysis also provide a clearer understanding of customer friction, helping teams identify process gaps and improve support performance.
For CX leaders, the launch enables phone support scaling without increasing staffing, training, and quality assurance costs, as higher resolution rates, greater consistency, and improved visibility offers more control over service quality.
