Background

A large manufacturing conglomerate with multiple facilities in Taiwan and overseas employs thousands of staff across its operations. Over years of operation, the organization had accumulated vast knowledge assets — including technical documents, standard operating procedures (SOPs), quality management records, and customer service cases. However, these resources were scattered across departmental file systems, email inboxes, and paper archives, creating severe knowledge silos.

New employees spent excessive time searching for the technical documents and operating procedures they needed, while the departure or retirement of senior staff often meant the permanent loss of valuable institutional knowledge. The organization urgently needed an intelligent knowledge management system capable of consolidating dispersed knowledge and providing employees with fast, convenient access.

Challenges Faced

• 數萬份技術文件分散於不同系統與部門，缺乏統一的搜尋入口
• Traditional keyword search cannot understand query semantics, causing employees to frequently fail to find the documents they actually need
• Documents exist in diverse formats including PDF, Word, Excel, presentations, and scanned images, making unified processing difficult
• Cross-department knowledge sharing mechanisms are weak, with different departments repeatedly reinventing solutions to the same problems
• Tacit knowledge held by senior employees is difficult to transfer, and talent attrition leads to knowledge gaps
• Document access permissions must be enforced across departments in compliance with enterprise information security policies

Industry Solutions

The company deployed LargitData's RAGi enterprise AI engine — a Retrieval-Augmented Generation platform — to establish a company-wide AI-powered knowledge management system. Leveraging advanced RAG technology, RAGi enables employees to interact with the enterprise knowledge base through natural language queries.

Implementation Overview

• RAGi Enterprise AI Decision Platform: centrally import documents from all departments to establish an enterprise-grade AI knowledge base
• Natural Language Query Interface：員工可使用日常用語提問，系統依設定檢索相關文件並生成回答，回答品質取決於知識庫收錄範圍與文件本身的完整度
• Multi-Format Document Processing: supports automatic parsing and indexing of PDF, Word, Excel, PowerPoint, images, and other document formats
• Access Control System：依據部門與職級設定文件存取權限，檢索結果與生成回答皆受同一套權限規則約束；上線前建議以各職級帳號實際測試隔離效果
• Source Citation Annotation：可依設定在回答中附上引用的原始文件與段落，讓使用者回查原文確認

RAGi Enterprise AI Retrieval-Augmented Generation Engine →

Implementation Results

• 員工查找技術文件與作業規範的資訊查找時間縮減 85%
• 成功匯入數萬份跨部門文件，建立可統一查詢的知識資產庫
• Over 3,000 AI queries per month, making the platform an indispensable tool for employees' daily work
• 新人上手時間縮短 40%，新進員工能自行查到 SOP 與過往案例，減少反覆詢問資深同仁
• 跨部門知識共享的情況增加，同一個問題被不同單位重複摸索的次數下降
• 資深員工的專業知識以文件與問答紀錄形式保存下來，降低人才流失造成的知識斷層風險

數字怎麼看：量測口徑與適用前提

85%、40% 與每月 3,000 次查詢，是該專案在特定期間、特定部門範圍內的量測結果。查找時間的改善以導入前後同一批使用者的實際作業計時為基準，涵蓋的是有明確答案且文件已收錄的問題；若問題本身牽涉判斷、或答案根本沒被寫進任何文件，AI 知識庫幫不上忙。上手時間的縮短同樣受招募條件、職務複雜度與帶人文化影響，換一家公司未必複製得出來。

規劃企業知識庫時，有幾個容易被低估的前提值得先確認。文件品質是第一個：版本混亂、內容互相矛盾的舊文件匯進去，只會讓系統穩定地給出錯誤答案，前置的文件盤點與去重往往比技術導入更花時間。第二是索引更新的時效，文件修訂到能被查詢之間有多久延遲，需要在合約或維運文件裡寫明。第三是引用覆蓋率，並非每個回答都必然附得上來源，應要求供應商說明無法定位來源時系統的行為，以及檢索失敗時是拒答還是硬答。第四是權限隔離，務必以不同職級的實際帳號測試，確認低權限使用者不會透過摘要間接讀到受限內容。