Manufacturing
Manufacturing Knowledge Management System
Transformed vast internal manuals and technical documents into AI-ready formats, building a knowledge management system with zero rework. Digitized veteran employees' tacit knowledge and significantly improved organization-wide operational efficiency.
Challenge
Decades of accumulated internal manuals and technical documents were scattered across inconsistent formats, making them difficult to utilize. The impending retirement of veteran employees posed a serious risk of losing critical institutional knowledge.
Tens of thousands of technical documents scattered across PDF, Word, and paper formats
Average of 30+ minutes spent searching for a single manual
Existing AI tools unable to structure manufacturing-specific drawings and tabular data
Risk of losing tacit knowledge as veteran employees retire
Solution
Using INDX's data structuring platform, we unified and structured technical documents across all formats. High-precision metadata tagging, including engineering drawings and specifications, enabled an instantly searchable knowledge base.
Automated structuring of all documents including PDFs, Word files, and scanned documents
High-precision analysis of manufacturing-specific drawings and specification tables
Multi-dimensional search system enabled by rich metadata
Integration with internal RAG system for conversational knowledge retrieval
Value of INDX Metadata Technology
INDX's proprietary metadata technology enables not just text search, but high-precision retrieval that understands document structure, context, and relationships. Manufacturing-specific terminology and engineering drawings are accurately structured.
Application Screenshots
Screen mockups of the applications we built. We design and develop UI/UX tailored to each client's workflow.
AC Motor Bearing Replacement Manual v3.2
Manual | Mechanical Design | Updated 2024.08
Bearing Inspection Checklist
Checklist | Quality Control | Updated 2024.06
Vibration Anomaly Response Flow
Procedure | Maintenance | Updated 2024.03
High-precision search combining metadata filters and natural language
System Architecture
We built an end-to-end system that structures scattered technical documents with INDX and delivers them as a RAG-based knowledge search system to employees.
Knowledge Search Portal
Multi-dimensional search combining natural language, keywords, and metadata filters. Results are displayed with relevance scores and highlight matching sections within documents.
Chat-based Q&A Interface
Employees ask questions in natural language and receive AI-generated answers grounded in relevant technical documents. Source document links and page numbers are cited to ensure reliability.
Document Viewer
View structured documents in the browser while preserving original layouts for drawings and tables. Metadata tag associations are visualized inline.
INDX Document Structuring Engine
Processes PDFs, Word files, and scanned images through OCR, layout analysis, and section splitting. Uses specialized models trained on manufacturing-specific drawing symbols and specification patterns.
Metadata Tagging Pipeline
Automatically assigns metadata such as document type, technical domain, product name, and process to structured documents. Hierarchical tag system enables multi-faceted search.
RAG Orchestration
Retrieves relevant documents through vector search and metadata filtering based on user queries, then provides context to LLMs. Manages answer generation and source tracking end-to-end.
Vector DB & Search Index
Combines a vector database for document embeddings with a full-text search index for metadata queries. Hybrid search achieves high retrieval accuracy.
Document Storage
Object storage for original files and structured data with version control to maintain document update history.
Data Flow
Results
Dramatically reduced time spent on manual search and reference
Dramatically improved access time to required technical documents
Achieved high-precision structuring of documents including drawings and tables