Customer service platform provider Kustomer has announced the general availability of Conversation Intelligence and AI QA.
Built on the vendor’s underlying AI Attributes framework, the native capabilities are designed to provide customer experience (CX) leaders with visibility across 100 percent of customer interactions, moving contact centers away from sample-based reporting and low-response customer surveys.
Conversation Intelligence is the type of AI Attribute that evaluates conversations, turning unstructured information into structured data through Classifiers, which sort conversations into labels a business defines; Scores, which evaluate a conversation against a weighted rubric and return a numeric score with a rationale; and Extractors, which pull out or summarize information based on natural-language instructions.
Complementing this, AI QA automatically scores customer interactions against defined criteria, incorporating weighted categories and auto-fail rules for compliance or mandatory standards. Rather than functioning as a black-box scoring tool, the system provides transparent rationale alongside each rating to assist supervisors during agent coaching.
Highlighting the risks of managing contact centers through limited datasets, Brad Birnbaum, CEO & Co-Founder at Kustomer, said:
Brad Birnbaum, CEO & Co-Founder at Kustomer
“Most CX teams are making six-figure staffing and strategy calls off the two or three percent of customers who answer a survey. The rest goes unmeasured. Conversation Intelligence and AI QA look at every conversation and show their work on every score, so leaders can coach against it and stand behind it. Both are built into the platform our customers already run on, so what we learn goes straight back into the data foundation behind their operations, not into a report nobody has time to read.”
By processing every interaction through Kustomer’s unified data model, the new features allow organizations to convert unstructured dialogue into real-time operational triggers, such as automated triage and routing.
According to the company, with Conversation Intelligence and AI QA CX leaders can: • Catch risk before it shows up in churn by scoring 100% of conversations instead of the 2-3% that generate a survey response. • Coach with evidence instead of hunches by reviewing exactly which criteria a conversation hit or missed, and why. • Fine-tune AI agents faster using the same scoring data applied to human-rep coaching. • Prove the team’s impact with data, not anecdote by resting every claim on the full picture instead of a sample.
Early adopters have reported measurable improvements in operational accuracy and routing precision.
Tom Rosenbaugh, Senior Business Operations Analyst at Turo, commented:
“Our agents were categorizing conversations manually, and we knew there was room for human error. When we compare that to what we’re seeing with Conversation Intelligence, the AI is categorizing conversations more accurately than we were able to with agents alone.”
Simon Birch, Head of Customer Experience at Loaf, added:
“Conversation Intelligence has given us a much smarter way to understand and route customer conversations. Rather than relying on keywords, it understands the intent behind why a customer is getting in touch, helping us get conversations to the right team more accurately and giving us much better insight into what our customers actually need.”
Conversation Intelligence and AI QA are available now. Both capabilities are introduced alongside Kustomer’s broader “ROI of CX” initiative, including the ROI of CX Report and the upcoming virtual ROI of CX Summit on October 20, 2026, which further explore the business case for data-driven CX investment.
Kustomer’s move reflects a broader, accelerating consolidation within the CX ecosystem: the convergence of core CRM/ticketing platforms with specialized conversation intelligence and automated quality assurance.
Applying unified evaluation frameworks to both human agents and automated bots creates a standardized benchmark across hybrid workforces, allowing organizations to pinpoint operational friction and retrain automated systems with the same precision they apply to human personnel.
