Industry Solutions

The manufacturing sector is under the dual pressure of knowledge transfer gaps and digital transformation. LargitData's RAGi enterprise AI knowledge base and QubicX on-premise AI platform help manufacturers consolidate technical documents, SOPs, and expert knowledge scattered across departments into an intelligent knowledge management system, enabling effective knowledge transfer and improved operational efficiency.

Industry Challenges

As manufacturing moves toward Industry 4.0, knowledge management and operational efficiency are the most pressing problems to solve:

• Severe Knowledge Silos：Critical documents — including engineering drawings, process parameters, quality specifications, and equipment maintenance manuals — are scattered across different departments and systems, requiring engineers to search across multiple systems to locate information, a process that is time-consuming and prone to gaps.
• Risk of Expert Knowledge Loss：When senior engineers and technicians retire or resign, the large body of tacit knowledge residing in their personal experience — such as troubleshooting techniques and process tuning know-how — is difficult to transfer effectively.
• Low Efficiency in Equipment Troubleshooting：When production line equipment malfunctions, maintenance personnel must leaf through thick equipment manuals or wait for senior engineers to come on-site, resulting in excessive production line downtime.
• Vast Volume of Quality Management Documents：ISO quality management system documents, customer audit files, and FMEA analysis reports are voluminous, making it difficult for quality personnel to locate what they need when preparing for audits or handling customer complaints.
• Insufficient Supply Chain Information Integration：Supplier specifications, incoming inspection reports, and contract terms are managed in disparate locations, making it difficult for procurement and quality teams to quickly compare and retrieve information.

Industry Solutions

LargitData provides knowledge management-centered AI solutions for the manufacturing industry:

RAGi — Intelligent Knowledge Base for Manufacturing

• Consolidates technical documents, equipment manuals, process SOPs, quality standards, and more into an AI-driven enterprise knowledge base.
• Engineers can quickly retrieve technical information through natural-language queries — for example, "Possible causes and troubleshooting steps for abnormal spindle vibration in a CNC machining center."
• AI automatically extracts answers from relevant documents and cites sources, ensuring full information traceability.
• Supports the digitization of senior engineers' troubleshooting experience and process-tuning knowledge, creating a permanent corporate knowledge asset.

Learn MoreRAGi Enterprise AI Retrieval-Augmented Generation Engine

QubicX — Factory On-Premise AI Deployment

• All AI computing runs on internal factory servers, ensuring confidential process parameters and design drawings never leave the premises.
• Even in restricted or air-gapped factory network environments, the AI system operates independently without relying on external cloud services.
• Supports integration with existing factory MES (Manufacturing Execution System), ERP, and PLM systems, fitting seamlessly into the existing IT architecture.
• Flexibly scalable based on factory size, supporting deployments from a single production line to multi-site operations.

Learn MoreQubicX On-Premise AI Platform

Diverse application scenarios

Scenario 1: Intelligent Equipment Fault Diagnosis

A semiconductor packaging factory loaded maintenance manuals, historical repair records, and troubleshooting experience for all equipment into the RAGi knowledge base. When production line equipment malfunctions, maintenance engineers on the factory floor can describe the fault in natural language via a mobile phone or tablet — for example, "Wire bonder, third track, intermittent wire feeding issues and occasional wire breaks" — and AI instantly cross-references historical cases in the knowledge base and recommends troubleshooting steps, reducing Mean Time To Repair (MTTR) by 45%.

Scenario 2: Rapid Onboarding of New Engineers

A precision machining factory faced a knowledge transfer crisis as senior master machinists retired one by one. The factory compiled decades of machining experience, tooling selection principles, and process parameter tuning tips from the masters and imported them into the RAGi knowledge base. When new engineers encounter problems during operations, they can consult the AI knowledge base at any time — as if a virtual senior master were always there to guide them.

Scenario 3: Fast Retrieval of Quality Audit Documents

An automotive parts supplier loaded IATF 16949 quality management documents, Customer Specific Requirements (CSR), PPAP documents, and historical audit records into the RAGi knowledge base. When customers conduct supplier audits, quality personnel can instantly query questions such as "What are the specific requirements for defect control under the Ford Q1 assessment?" and AI quickly compiles the answer from relevant documents, significantly improving audit-response efficiency.

Scenario 4: Unified Supplier Information Management

Procurement and quality teams loaded specifications, Material Safety Data Sheets (MSDS), incoming inspection reports, and contract terms for all suppliers into the RAGi knowledge base. When identifying alternative suppliers or comparing material specifications, a simple natural-language query returns cross-supplier comparison information, replacing the inefficient practice of manually reviewing paper or electronic files one by one.

Scenario 5: Process Standardization and Best Practice Sharing

A multinational manufacturing group loaded process SOPs, Kaizen improvement records, and best-practice cases from each plant into a unified RAGi knowledge base. Engineers at Plant A can query "How did Plant B resolve surface roughness defects on the same product?" — promoting cross-plant knowledge sharing and process standardization.

Expected Outcomes

• Equipment Fault Repair Time Reduced by 40-50%：The AI knowledge base gives maintenance personnel instant access to troubleshooting recommendations, significantly reducing production line downtime losses.
• New Employee Training Cycle Shortened by 30%：The AI assistant provides technical guidance on demand, accelerating the speed at which new engineers become independently operational.
• Permanent Preservation of Knowledge Assets：Converts the tacit knowledge of senior employees into a digital knowledge base, preventing knowledge loss due to staff turnover.
• Audit Preparation Time Reduced by 50%：AI rapidly compiles quality documents and historical records, greatly improving audit response efficiency.
• How is data security ensured?：QubicX on-premise deployment ensures that confidential process parameters and engineering drawings remain within the factory, eliminating the risk of data leakage.