Implementation of a Knowledge Base for Tens of Millions of Unstructured Documents Drives Full-Chain Digital-Intelligent Upgrade of a Manufacturing Group
For a large manufacturing group's more than ten million document resources across 12+ categories, including journal articles, conference papers, books, standards, and regulations, with an unstructured data vectorization engine as the core, a three-tier enterprise–department–individual industry-level knowledge base was built, enabling cross-database vector retrieval in seconds, AI quality inspection and proofreading, and agent workflow automation, increasing data resource utilization from less than 10% to more than 95%.
Background and Challenges
A large manufacturing group has more than 10 million document resources across over 12 categories, including journal articles, conference papers, books, standards, and internal policies, 30% of which is unstructured content such as scanned PDFs and engineering drawings. These documents were previously scattered across departmental servers and employees' personal computers, lacking unified archiving standards and a unified search access point, resulting in a data resource utilization rate of less than 10%. The group faces systemic bottlenecks in scientific research literature search, production compliance referencing, and knowledge system accumulation: massive volumes of literature cannot be quickly located; scanned documents and drawings are difficult to search as text; departmental knowledge bases are fragmented; and metadata extraction and classification rely on inefficient manual operations. The enterprise urgently needs an unstructured knowledge base platform to uniformly integrate scattered document resources, create vector indexes, and manage them intelligently.
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Literature resources are dispersed and fragmented.
Tens of millions of documents are scattered across departmental servers and employees' personal computers, lacking unified archiving standards and a search entry point.
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Unstructured content is difficult to retrieve.
30% are PDF scans, engineering drawings, etc., which cannot be directly converted into text for retrieval, rendering traditional keyword matching ineffective.
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Resource utilization is extremely low.
Data resource utilization is less than 10%, and a large amount of high-value scientific research literature and standards/regulations remain dormant and unused.
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High cost of manual processing.
Metadata extraction, knowledge system classification, quality inspection and proofreading, and other processes rely on manual operations, resulting in low efficiency and high error rates.
Solution
Centered on an unstructured data vectorization engine, we built an end-to-end platform covering data ingestion, knowledge base management, and intelligent applications. Over 500,000 unstructured data items have been ingested and vectorized, with extraction accuracy reaching 95%; an industry-level knowledge base with three tiers—enterprise, department, and individual—has been built to enable cross-database vector retrieval within seconds; and an AI quality inspection and review module and agent workflows have been added to make processes such as metadata extraction, knowledge system classification, and quality inspection report generation fully intelligent.
Data Ingestion and Vectorization Layer
Millions of documents uniformly collected and integrated; OCR recognition and extraction for PDF scans; structured parsing of engineering drawings; vectorized embedding, 95% accuracy
Three-Tier Knowledge Base and Retrieval Engine
Enterprise-level knowledge base (shared across the entire group); department-level knowledge base (business isolation); personal-level knowledge base (private space); cross-database second-level vector retrieval
Agent Applications and Management Layer
AI quality inspection report auto-generation; AI one-click review and correction of recognition errors; agent workflows (metadata extraction/classification); workbench quality ranking and capacity monitoring
Core characteristics
Vectorization of massive unstructured data
Completed the ingestion and vectorization of over 500,000 pieces of unstructured data, covering more than 12 categories of documents such as journal articles, conference papers, books, standards, and policies. For the 30% of data in complex formats such as scanned PDFs and engineering drawings, text content was extracted through OCR and structured parsing. The vector embedding accuracy reached 95%, laying a high-quality data foundation for subsequent retrieval and applications.
Level 3 industry-level knowledge base
Build an enterprise-department-individual three-tier industry-level knowledge base system: enterprise-level knowledge bases enable document sharing across the entire group, department-level knowledge bases support business isolation and permission control, and individual-level knowledge bases provide private knowledge spaces. The three-tier knowledge bases support cross-base second-level vector retrieval, reducing the time to find materials from an average of 30 minutes to 3 seconds, enabling efficient circulation and precise reach of knowledge assets.
AI Quality Inspection and One-Click Proofreading
The quality management module uses AI to automatically generate quality inspection reports, performing quality assessment and issue annotation on vectorized extraction results. The AI one-click proofreading function can automatically correct source file recognition errors, including OCR recognition deviations, format parsing omissions, missing metadata, and other issues, improving overall data quality by 65% and significantly reducing manual verification workload and error rates.
Agent Workflow Automation
Agent workflows fully intelligentize processes such as metadata extraction and knowledge taxonomy classification: automatically identifying document types and keywords, extracting metadata such as title, author, abstract, and source; automatically categorizing according to the disciplinary classification system and building a knowledge association graph. The dashboard presents knowledge quality rankings and vector capacity monitoring in real time, providing management with a precise basis for decision-making and reducing labor costs by 90%.
Profit and value
Data resource utilization jumped from less than 10% to more than 95%, saving over RMB 5 million in labor costs annually, and driving digital-intelligent upgrades across the entire chain of scientific research literature knowledge R&D, production, and compliance.