Quant launches agentic AI for service. New “digital colleagues” are built to resolve issues end-to-end in real time across phone, chat and—soon—video.
Early performance claims. Quant says production deployments are resolving ~77% of issues with sub-5-second responses, outpacing human agents by 13 points.
Consumer trust remains a hurdle. In a Quant-commissioned U.S. survey (n=1,500, Sept. 21–22, 2025), 82% say chatbot sessions usually escalate to humans, while 60% say “accurate resolution” matters most.
Quant unveiled its first agentic AI system for customer service, positioning it as a digital workforce that “gets things done” rather than merely generating answers. The company says its system executes tasks—booking, rerouting, safeguarding accounts—so customers avoid loops and dead ends. Quant, led by CEO and Chairman Chetan Dube (formerly founder of IPsoft/Amelia), has been promoting agentic AI as the next phase of enterprise automation.
According to Quant, new research highlights friction in today’s support experiences: 57% of consumers report “disastrous” service interactions; only 31% rate speed as the top priority; 60% value accurate resolution most; twice as many prefer sharing sensitive details with humans as with current AI (40% vs. 20%); and 82% say chatbot conversations usually escalate to a live agent.
The company argues that reliable, end-to-end digital resolution would increase customer loyalty for 72% of consumers. (Study of 1,500 U.S. adults by Sago UK, fielded Sept. 21–22, 2025; commissioned by Quant.)
Impacted Audiences for Quant’s Agentic Service
Contact center operational leaders
CX and service executives focused on resolution and loyalty
Technical builders integrating AI with CRM/CCaaS and back-office systems
Industry Context: From Answers to Actions in the Contact Center
Agentic approaches aim to shift AI from information retrieval to workflow completion (e.g., policy changes, account actions). Enterprise deployments are already resolving a good chunk of complex utility calls, and agentic AI is where companies see strong ROI compared with traditional generative assistants.
This aligns with broader market movement toward proactive, automated service while keeping human agents on complex, emotionally nuanced interactions—trends CMSWire has covered extensively in contact center and CX reporting.
Related Article: From Reactive to Proactive: Strategies for Anticipating Customer Needs
24/7 Digital Colleagues, Built for Resolution
Generative AI provides answers. Agentic AI gets things done.
– Chetan Dube, CEO & Chairman
Quant
Quant Service Capabilities
Quant’s launch emphasizes task completion and multi-channel coverage:
CapabilityDescriptionReal-time resolutionExecutes actions (e.g., rebooking, payment plans, rerouting bags) to close the loop without hand-offs.Multi-channel operationsWorks as a phone agent or chat assistant; video avatar planned to read tone/expression.High first-contact solveQuant cites ~77% issue resolution in production and sub-5-second response times.Use-case breadthUtilities (e.g., surge & storm events), healthcare scheduling, QSR order handling (reportedly 1M+ orders at >90% accuracy).Human-AI teamingAutomates repetitive tasks so staff focus on retention, proactive outreach and complex care.
Quant AI powers Verizon’s AI Assistant, providing instant help with connectivity, personalized plan recommendations and around-the-clock virtual support.
Agentic AI Emerges as Customer Service Solution
Quant’s unveiling of its first agentic AI for customer service reflects a broader industry response to widespread customer frustration. The company’s new research found that 57% of consumers experienced disastrous customer service encounters, highlighting significant problems with current systems.
Industry Shift Beyond Speed
The research reveals changing customer priorities. Only 31% of consumers now rank speed as their top concern, while 60% prioritize accurate issue resolution. This shift reflects growing dissatisfaction with traditional systems that bounce customers between agents or trap them in repetitive loops.
Technology Capabilities
Unlike conventional chatbots that follow rigid scripts, agentic AI systems can reason and adapt in real time. These systems use contextual information and interaction history to deliver personalized support, according to industry analysts.
Market Projections
Gartner predicts agentic AI will autonomously resolve up to 80% of common customer issues by 2029. Early adopters report reduced call times, higher satisfaction scores and more empowered customer service agents.
Competitive Implications
Industry experts suggest companies that fail to adopt agentic AI technology risk falling behind as customer expectations continue evolving. The technology represents a fundamental shift from speed-focused metrics to accuracy-based service delivery.
Learning OpportunitiesView all
Early Customer Example (Utility)
Quant points to a large U.S. utility (PPL serves ~3.6M customers across several states) as evidence that agentic automation can scale during storms and surges, with higher CSAT than human-only handling.
Why It Matters
Resolution as the north star. Consumers say accuracy and completion trump speed—an opening for agentic AI to prove value.
Trust must be earned. With most chatbot sessions reportedly escalating, measurable outcomes and governance will determine adoption.
ROI pressure. Enterprise buyers will look for production-grade evidence (FCR, AHT, CSAT, containment, cost-to-serve) rather than demos.
Who Is Quant?
Quant is an agentic AI company founded by Chetan Dube (ex-IPsoft/Amelia), focused on “digital employees” that act autonomously to complete service tasks. Dube has argued publicly that agentic systems deliver materially higher ROI than answer-only assistants and will define hybrid human-AI work by 2030.
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