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AI-Powered Secure Document Intelligence for Automotive Engineering

The client is a leading automotive manufacturing organization based in the United States that supports large engineering teams responsible for vehicle design, production, and process optimization.
The organization relies on extensive proprietary documentation, including technical manuals, schematics, diagrams, and process documents, which must remain secure while being easily accessible to authorized teams.

Business Challenge

A secure, intelligent system was required to protect sensitive knowledge while making it easier for engineers to retrieve accurate, contextual information.

The organization faced multiple operational and knowledge-management challenges:

High risk of intellectual property exposure through traditional file-sharing methods
Excessive time spent by senior engineers responding to routine technical queries
Limited visibility into document access and usage patterns
Difficulties onboarding new engineers due to complex and fragmented documentation
Inefficient handling of visual technical content, such as diagrams and schematics

Project Overview

SilwaTech was engaged to design and implement an AI-powered document assistant tailored for automotive engineering environments. The platform was designed to:

  • Securely manage proprietary technical documentation
  • Enable fast, context-aware knowledge retrieval
  • Support visual content such as diagrams and images
  • Track and audit document access and usage

The solution followed a human-in-the-loop approach, ensuring AI-generated responses remained traceable, verifiable, and governed.

Case Study Topics

Solution Delivered

Secure Document Ingestion & Processing

A robust document processing layer was implemented to handle complex technical content:

  • Ingestion of multi-format documents
  • Text and image extraction from manuals, schematics, and diagrams
  • Metadata enrichment and vectorization for semantic search
  • Version-aware document management

AI-Assisted Knowledge Retrieval

The platform enables engineers to retrieve precise, contextual answers:

  • Natural language queries answered using AI-assisted retrieval
  • Contextual responses supported by relevant diagrams and images
  • Source attribution allowing users to verify original documents
  • Relevance ranking to surface the most accurate information

Intellectual Property Protection & Access Control

Strong security controls were embedded throughout the system:

  • Role-based access control restricting document visibility
  • Secure authentication and authorization workflows
  • Query logging, access monitoring, and usage analytics
  • Activity tracking to support audits and compliance

Visual Content Understanding

To address complex engineering visuals:

  • Integrated image analysis for diagrams and schematics
  • Clear labelling and association of visuals with textual explanations
  • Improved clarity and usability of visual technical documentation

Continuous Learning & Improvement

  • User feedback captured via secure APIs
  • Continuous refinement of AI responses for accuracy and relevance
  • Ongoing updates to adapt to evolving engineering knowledge and workflows

Technology Stack

  • AI & NLP: OpenAI GPT-4, LangChain
  • Backend: FastAPI with secure API orchestration
  • Frontend: ReactJS web application
  • Vector Storage: Qdrant for semantic retrieval
  • Image Analysis: AWS Rekognition
  • Cloud Infrastructure: AWS (compute, storage, auto-scaling)
  • Security: OAuth 2.0, role-based access control, encryption at rest and in transition

Engagement Model

Enterprise AI solution delivery with a cross-functional team spanning AI engineering, document processing, backend and frontend development, UX design, and security architecture, aligned to iterative delivery and continuous feedback cycles.

Security & Compliance Focus

Given the sensitivity of proprietary automotive documentation, security and compliance were foundational.

 

Key focus areas included:

  • Encryption of data at rest and in transit
  • Role-based access aligned to organizational responsibilities
  • Detailed audit logs for document access and AI interactions
  • Secure authentication and authorization mechanisms
  • Regular security reviews and monitoring

 

These measures ensured intellectual property protection while enabling controlled knowledge sharing.

Value Delivered

  • Significantly reduced time spent on routine technical queries
  • Improved accessibility of complex technical and visual documentation
  • Strengthened protection against unauthorized document downloads
  • Faster onboarding and improved productivity for new engineers
  • Scalable, cloud-based platform supporting concurrent users
  • An adaptive AI system that improves continuously through feedback

Why SilwaTech

SilwaTech was selected for its ability to combine AI intelligence with enterprise-grade security, including:
Experience building secure AI platforms for IP-sensitive industries
    Strong expertise in document intelligence and contextual retrieval
      Capability to integrate NLP with visual content understanding
        Emphasis on auditability, access control, and responsible AI usage

          Looking to Secure and Scale Knowledge Access with AI?

          SilwaTech helps enterprises build secure, intelligent document platforms

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