Intelligent Document Management & AI Assistant for Automotive Manufacturing

About the Project
Industry: Automotive Manufacturing
Solution Type: Document AI Assistant
AI Technology: OpenAI GPT-4, LangChain, AWS Rekognition
Other Technologies: NodeJS, ReactJS, AWS, Qdrant
Integrations: Document Processing, Image Analysis, Access Management

Problem Statement

Challenge Description
Challenge Description

A major automotive parts manufacturer needed to secure proprietary production documents while enabling efficient knowledge sharing across engineering teams. Traditional file-sharing methods posed risks, and knowledge retrieval was time-consuming, often requiring senior staff involvement.

Key Pain Points
Key Pain Points
  • Risk of intellectual property leakage through direct file downloads.
  • Excessive time spent by senior engineers on routine queries.
  • Difficulty tracking document access and usage.
  • Challenges in transferring knowledge to new team members.
  • Managing complex visual technical documentation.

Solution Overview

We developed an AI-powered document assistant for automotive manufacturing that combines advanced document processing, security, and knowledge retrieval, ensuring secure and efficient access to technical documentation and knowledge sharing across teams.
High-Level Architecture

High-Level Architecture

  • Document Processing Layer: Multi-format document ingestion, Image extraction and analysis, Text and image vectorization, Metadata extraction.
  • Security Layer: Role-based access control, Query logging and monitoring, Usage analytics and activity tracking.
  • Knowledge Retrieval: Contextual answers with image and diagram support, Source attribution for verification, Relevance ranking.
Key Features

Key Features

  • Document Management: Multi-format support (PDF, DOC, PPT), Automated image processing, version control, and metadata management.
  • AI Assistant: Natural language queries, context-aware responses, visual content inclusion, and source referencing.
  • Security & Analytics: Granular access control, usage tracking, query analytics, and performance monitoring.

Lessons Learned

Key Insights
  • Image processing is crucial for technical documentation.
  • Context retention enhances response quality for complex queries.
  • User behavior analytics provides valuable feedback for improvement.
  • Balancing security with accessibility optimizes usability.
Best Practices
  • Regular system training with new documents.
  • Monitoring query patterns for relevance.
  • Conducting regular security audits.
  • Integrating user feedback.

Expected Outcomes and Metrics

Quantitative Results
  • 90% reduction in document download risks
  • 70% less time spent by senior staff on routine queries
  • 60% faster knowledge retrieval
  • 85% answer accuracy, including visual content
Qualitative Benefits
  • Strengthened intellectual property security
  • Improved knowledge accessibility
  • Smoother onboarding experience
  • Insights for continuous documentation improvement
Technical Integration
Document Processing
  • check
    Automated content extraction
  • check
    Image analysis pipeline
  • check
    Metadata processing
  • check
    Version management
Security Implementation
  • check
    Role-based access control
  • check
    Activity monitoring
  • check
    Audit logging
  • check
    Usage analytics
Analytics System
  • check
    Query tracking
  • check
    Usage patterns
  • check
    User engagement
  • check
    Content relevance
Intro
Plus
Pro
10-12 weeks
Features (includes all Intro features, plus):
  • Image processing
  • Advanced security
  • Detailed analytics
  • Custom roles
compare packages
6-8 weeks
Features:
  • Basic document processing
  • Text-based Q&A
  • Simple user management
  • Basic analytics
10-12 weeks
Features (includes all Intro features, plus):
  • Image processing
  • Advanced security
  • Detailed analytics
  • Custom roles
14-16 weeks
Features (includes all Advanced features, plus):
  • Multi-language support
  • Advanced image analysis
  • Custom integrations
  • Advanced analytics
Related Case Studies

FAQ

How does the system maintain document security while still providing information to users?
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Can the system understand technical drawings and diagrams?
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What happens if the AI provides incorrect or incomplete information?
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How does the analytics system help improve documentation?
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How long does it take to process new documentation into the system?
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Can we control which users have access to specific types of information?
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