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

• Tens of thousands of technical documents scattered across systems and departments, lacking a unified search portal
• 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: Employees can ask questions in natural language, and the system retrieves relevant documents and generates answers based on settings, with answer quality depending on knowledge base coverage and document completeness
• Multi-Format Document Processing: supports automatic parsing and indexing of PDF, Word, Excel, PowerPoint, images, and other document formats
• Access Control System: Document access permissions are set by department and job level, and both retrieval results and generated answers are bound by the same permission rules; before going live, we recommend actually testing the isolation with accounts at each job level
• Source Citation Annotation: Can be configured to attach cited source documents and paragraphs in responses, enabling users to trace back and verify original texts

RAGi Enterprise AI Retrieval-Augmented Generation Engine →

Implementation Results

• Employee time spent looking up technical documents and operating procedures was reduced by 85%
• Successfully imported tens of thousands of cross-departmental documents, establishing a unified knowledge asset repository
• Over 3,000 AI queries per month, making the platform an indispensable tool for employees' daily work
• New hire ramp-up time was shortened by 40%, as new employees can look up SOPs and past cases on their own, reducing repeated questions to senior colleagues
• Cross-departmental knowledge sharing increased, reducing repetitive troubleshooting of the same issue across different units
• Senior employees' expertise is preserved through documentation and Q&A logs, reducing the risk of knowledge gaps from staff turnover

How to read these numbers: measurement basis and preconditions

The 85%, 40%, and 3,000 monthly queries are measurement results from this project, within a specific period and specific department scope. The improvement in lookup time is benchmarked against actual timed work by the same group of users before and after deployment, covering questions with a clear answer that's already in the documentation; if a question itself requires judgment, or the answer was never written into any document, the AI knowledge base can't help. The reduction in ramp-up time is likewise influenced by hiring criteria, job complexity, and mentoring culture, and may not be reproducible at a different company.

When planning an enterprise knowledge base, there are several easily underestimated preconditions worth confirming first. Document quality is the first: importing old documents with messy versioning and contradictory content will only make the system reliably give wrong answers, and the upfront document inventory and deduplication work often takes longer than the technical rollout itself. Second is index-update latency — how long it takes between a document being revised and becoming queryable needs to be spelled out in the contract or operations documentation. Third is citation coverage: not every answer necessarily comes with a source, so you should require the vendor to explain how the system behaves when it can't locate a source, and whether it declines to answer or forces an answer when retrieval fails. Fourth is permission isolation — you must test with actual accounts at different job levels to confirm that lower-privilege users can't indirectly read restricted content through a summary.

From document inventory to a governed knowledge service

The highest-impact step often happens before RAG: inventory documents by owner, business purpose, confidentiality, version and retention status. Duplicate, obsolete and contradictory files should be resolved rather than indexed together. The team should then define question groups and a baseline set containing answerable, unanswerable, ambiguous and permission-sensitive questions. This makes retrieval quality measurable and prevents a polished demo from becoming the acceptance standard.

Production operation needs named owners for source updates, access changes, evaluation and incident handling. Monitor citation coverage, answer usefulness, retrieval misses, unauthorized-access attempts and the delay from document approval to searchable availability. Feedback buttons are not enough: reviewer corrections need a queue, a disposition and a retest path so approved improvements can be distinguished from preference changes.

• Inventory and deduplicate documents before indexing.
• Assign owners, versions, confidentiality labels and retention rules.
• Evaluate answerable, missing, conflicting and restricted questions.
• Test access with accounts from different departments and seniority levels.
• Track citation coverage, retrieval misses, freshness and correction closure.