SINGAPORE, May 19, 2026 /PRNewswire/ -- At the&nbsp;One North Foundation AI Community Gathering on May 14, 2026, WIZ.AI introduced Wizlynn, a multi-agent inbound platform built to help enterprises deploy GenAI in real customer service operations, not just in demos or pilots. <div class="PRN_ImbeddedAssetReference" id="DivAssetPlaceHolder1"> <a href="https://mma.prnasia.com/media2/2981997/20260518_125947.html" target="_blank" rel="nofollow" style="color: #0000FF"> <img src="https://mma.prnasia.com/media2/2981997/20260518_125947.jpg?p=medium600" title="WIZ.AI Launches Wizlynn: The Multi-Agent Inbound Platform Built for Enterprise Production" alt="WIZ.AI Launches Wizlynn: The Multi-Agent Inbound Platform Built for Enterprise Production" /> </a> WIZ.AI Launches Wizlynn: The Multi-Agent Inbound Platform Built for Enterprise Production </div> Wizlynn focuses on three core value propositions: reliability in conversation and result, dialect fluency, and fast deployment. It supports real-time customer conversations, enterprise system integration, human agent handoff, and enterprise-ready AI operations, helping companies move from basic chatbots to AI systems that can support live service workflows. Reliable in Both Conversation and Result Reliability is the first priority for enterprise GenAI, especially in financial services. Wizlynn is designed to deliver conversations that enterprises can trust and outcomes that customers can rely on. The platform supports fast responses under 2 seconds, 95% accurate intent recognition, natural interruption handling, compliance guardrails, and smooth transfer to human agents when needed. It is also built with strong data protection, AI security safeguards, stable system availability, and the ability to handle many customer conversations at the same time, making it suitable for regulated and high-volume service environments. In testing, Wizlynn achieved a 92.5% AI Resolution Rate, showing its ability to resolve many customer requests without human support. When escalation is needed, it targets up to 95% successful transfer to the right human agent. The platform includes more than 40 specialised AI agents for key banking scenarios, including account enquiries, card services, deposits, transfers, loans, payments, verification, limit changes, self-service requests, and escalation to human supervisors. Built for Real Enterprise Customer Service The launch featured a banking service demo showing how Wizlynn can identify customer intent, answer questions using a knowledge base, and retrieve live data from core systems when needed. The demo also highlighted Wizlynn's dialect fluency. In Southeast Asia, customers often switch between languages, dialects, and accents in one conversation. Wizlynn is built to handle this naturally, helping customers feel understood without forcing them into clean, single-language conversations. Fast Deployment: Live Within 2 Days, Full Service the Following Week WIZ.AI also introduced its Triple-Flywheel Engine, supported by three AI-native tools: AI Builder, AI Simulation, and AI Evaluation. These tools help enterprises build service scenarios, test AI responses, check quality, and improve performance before and after launch. This approach helps shorten implementation time, allowing Wizlynn to go live within 2 days, with full service ready the following week. After launch, the Triple-Flywheel Engine continues improving performance based on real production interactions. From Chatbot to Multi-Agent Platform The launch of Wizlynn reflects a broader shift in enterprise AI. Companies are moving beyond simple chatbots toward multi-agent systems that understand business context, connect with enterprise systems, and deliver measurable service outcomes. For WIZ.AI, Wizlynn marks the next step in enterprise GenAI: a platform designed not only to talk, but to work reliably in production. Find out more at <a href="http://www.wiz.ai/" target="_blank" rel="nofollow" style="color: #0000FF">www.wiz.ai</a>

WIZ.AI Launches Wizlynn: A Reliable Multi-Agent Inbound Platform for Enterprises

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