March 11, 2026
CallMiner’s AI Classifiers Now Include Automatic Sentiment Analysis
Customer experience platform CallMiner has unveiled a package of new and enhanced AI capabilities designed to give organisations a clearer picture of what is happening across their customer interactions. Central to the announcement is an expanded set of advanced AI classifiers, with whole-contact sentiment analysis now added to the platform for the first time.
The Problem with Sampling
Most organisations face the same structural challenge. Quality teams cannot listen to every call, and turning customer data into action remains one of the industry’s most persistent challenges.
Traditional survey tools add some visibility, but they have well-documented limits. They only capture feedback from customers who choose to respond, and that group tends to skew towards those at the extremes: either very satisfied or very dissatisfied. It is a well-known limitation that the broad middle ground goes largely unrecorded.
AI classifiers have been developed to address exactly this gap. Rather than reviewing a sample, they process every interaction automatically, sorting conversations into categories based on content, tone, intent, or outcome. Unlike older keyword-based systems, which would flag any call containing the word ‘cancel’ regardless of context, AI classifiers understand language in context, making them far more accurate.
The underlying capability goes by different names across the industry. Verint calls it speech analytics and interaction analytics, while NICE uses automated quality management, reflecting how widely adopted the technology has become, even if the terminology has yet to standardise.
What’s New?
CallMiner already offered classifiers covering reason for contact, outcome, and named entities. The new addition is whole-contact sentiment analysis, which the company says goes further than conventional approaches have been able to achieve. You can see how it stacks up against other sentiment analysis tools across the market.
Sentiment detection is harder than it sounds. According to CallMiner, meaning shifts depending on context, a customer’s tone can change several times within a single call, and specialised industry vocabulary can trip up systems that were not built with it in mind. Brief interactions, such as voicemails or short chat exchanges, add further complexity.
The updated classifier is built to handle these scenarios, picking up positive, neutral, and negative signals across different channels and languages. CallMiner has also designed the tool with regulatory requirements in mind, with explainability and human oversight built into its architecture in line with frameworks, such as the EU AI Act.
Beyond sentiment analysis, CallMiner has also made its interaction summaries more flexible. Rather than offering a single fixed format, organisations can now shape how AI-generated summaries are structured to suit their own workflows and compliance requirements. Agents benefit from an immediate overview of a customer’s background at the start of a conversation, without having to piece that picture together manually.
Bruce McMahon, Chief Product Officer at CallMiner, looked to the platform’s wider direction: “We remain focused on strengthening our foundational intelligence layer, enabling smarter CX automation, agent augmentation, and agentic AI discovery, and helping organisations achieve measurable improvements in efficiency and customer experience.”
From Insight to Action
The classifier upgrades feed into how CallMiner’s agentic AI turns insights into actions, where analysis does not simply appear in a report but drives automated workflows across business systems. The new sentiment data is accessible through CallMiner AI Assist, the platform’s conversational AI interface, as well as through a range of dashboard views and third-party integrations. A recent example of this is CallMiner’s partnership with Alvaria, combining its conversational intelligence with Alvaria’s outbound engagement and compliance capabilities.
As AI tools become more capable of reading not just what customers say but how they say it, the influence of customer interactions on business decision-making continues to grow.
