Announcement • Sep 08

Luda Technology Group Limited Implements AI-Enabled Multi-Agent Inquiry And Quotation System

Luda Technology Group Limited announced the implementation of an AI-enabled inquiry and quotation system, advancing the Company's digital transformation initiative and strategy to drive operational efficiency and scalable growth. The new system has delivered an immediate and measurable productivity gain, reducing the average quotation processing time from approximately 30 minutes to 3 minutes. By accelerating routine quotation work, the system is designed to help the Company respond to international stockists faster, handle higher inquiry volumes without a corresponding increase in headcount, and direct experienced personnel toward technical review, customer engagement, and commercial decision-making to drive new sales opportunities. Previously, quotations required the Company’s staff to manually review customer inquiries, spreadsheets, and internal records to identify product specifications, item weights, and pricing inputs. The new AI system interprets incoming inquiries, searches validated internal data sources, and generates a preliminary quotation for review. The system also develops and maintains client-specific inquiry profiles based on approved historical interactions, enabling the system to recognize each client's usual inquiry format, commonly requested products, preferred standards, and customary units of measurement. This client-level personalization is designed to improve product matching and quotation precision to drive customer retention. The system operates under a human-in-the-loop framework that preserves commercial and technical oversight. Every preliminary quotation is checked and confirmed by authorized Company personnel before release, preserving commercial and technical oversight. The system also features a controlled, closed-loop learning mechanism whereby approved corrections made by staff to preliminary quotations are captured and used to improve the accuracy of future quotations. It applies that feedback to similar future inquiries, progressively reducing errors and compounding the system's accuracy over time. The system is powered by Moonshot AI’s Kimi's K3 model, and incorporates a multi-agent architecture, retrieval-augmented generation, intelligent data search, and rules-based pricing logic.

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