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Silwatech is backed by a team of 1000+ employees, serving clients in over 10+ industries across 9+ locations
Secure AI Document Intelligence Platform for Automotive Engineering
The end client is a US-based automotive manufacturer with engineering teams supporting vehicle design, manufacturing and process optimisation.
The organisation manages substantial volumes of proprietary technical information, including engineering manuals, process documents, schematics and technical diagrams. This information needed to remain protected while being readily accessible to authorised employees.
The end client’s identity is withheld due to contractual confidentiality and NDA obligations.
Project Overview
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- The project involved the development of an AI-powered document-intelligence platform for automotive engineering teams.
The solution was designed and developed by a SilwaTech partner company through SilwaTech’s partner delivery ecosystem.
The platform covered
- Secure ingestion of proprietary engineering documents
- Processing of text, images, diagrams, and schematics
- Natural-language search across technical knowledge
- Context-aware answers supported by source references
- Role-based access to restricted information
- Monitoring and auditing of document usage
- User feedback and controlled knowledge improvement
The platform followed a human-in-the-loop approach so that engineers could verify AI-generated responses against the underlying documents and supporting technical content.
- The project involved the development of an AI-powered document-intelligence platform for automotive engineering teams.
Case Study Topics
Business Challenge
- Managing proprietary engineering knowledge created several operational and security challenges.
Key challenges included
- Risk of intellectual property exposure through conventional file-sharing methods
- Large volumes of fragmented technical documentation
- Senior engineers spending significant time answering routine questions
- Difficulty locating accurate information across different document formats
- Limited visibility into document access and usage
- Lengthy onboarding for new engineers
- Inefficient search across diagrams and technical schematics
- Need to ensure that AI-generated responses remained traceable to approved sources
The client needed a secure document-intelligence platform that could improve access to engineering knowledge without weakening intellectual property controls.
Solution Architecture
Solution Delivered
Secure Document Ingestion and Processing
A controlled ingestion pipeline prepared engineering documents for intelligent search and retrieval.
Key capabilities included
- Ingestion of multiple document formats
- Text extraction from manuals and process documents
- Image extraction from technical files
- Processing of schematics and engineering diagrams
- Metadata enrichment
- Document segmentation and vectorisation
- Version-aware document management
- Controlled indexing of approved information
AI-Assisted Knowledge Retrieval
A conversational search interface allowed engineers to retrieve relevant technical information using natural-language questions.
The solution provided
- Natural-language search across engineering documentation
- Contextual answers generated from approved sources
- Semantic retrieval of relevant document sections
- Source attribution for AI-generated responses
- Relevance ranking of retrieved information
- Links to supporting documents and visual content
- Faster access to routine technical guidance
- Human verification of retrieved information
Intellectual Property Protection and Access Control
Security controls protect proprietary engineering information and restrict access according to organisational responsibilities.
Capabilities included
- Secure user authentication
- Role-based access control
- Document-level permissions
- Controlled access to sensitive information
- Query and interaction logging
- Monitoring of document access
- Usage analytics
- Activity tracking for audit and compliance purposes
- Controls designed to reduce unauthorised downloads and data exposure
Visual Content Intelligence
Computer vision capabilities made diagrams and schematics more accessible through the same search experience.
The platform supported
- Analysis of engineering diagrams and schematics
- Identification of visual elements
- Extraction of labels and associated information
- Linking of images with related document text
- Retrieval of relevant visuals alongside written answers
- Improved usability of image-intensive technical documentation
Continuous Improvement
A governed feedback process helped improve the relevance and usefulness of the assistant over time.
The process included
- User feedback captured through secure interfaces
- Review of incomplete or inaccurate responses
- Refinement of retrieval logic
- Improvement of relevance ranking
- Updates as approved engineering knowledge changed
- Controlled re-indexing of revised document versions
- Monitoring of recurring information needs
Technology Stack
The technology environment included
- OpenAI GPT-4 for natural-language processing and response generation
- LangChain for retrieval orchestration and AI workflows
- Qdrant for vector storage and semantic search
- AWS Rekognition for visual-content analysis
- FastAPI for backend services and API orchestration
- React for the engineer-facing web application
- AWS compute, storage and auto-scaling services
- OAuth 2.0 for secure authentication
- Role-based access control
- Encryption of data in transit and at rest
- Logging and monitoring services for auditability
Engagement Model
The project was delivered through SilwaTech’s partner ecosystem under a partner-led implementation model.
The solution architecture, AI development, document-processing pipeline, visual-analysis capabilities, application development, testing and technical implementation were performed by an experienced SilwaTech partner company. SilwaTech contributed through engagement alignment, partner collaboration, and coordination of the required document-intelligence, security, and enterprise AI capabilities.
The cross-functional delivery team included specialists in
- AI and large language model engineering
- Document processing and semantic retrieval
- Computer vision and visual-content analysis
- Backend and frontend development
- User-experience design
- Cloud infrastructure
- Security architecture
- Quality assurance and AI-response evaluation
Delivery followed an iterative approach, beginning with secure document ingestion and retrieval before progressing to visual-content support, access monitoring, feedback capture and continuous improvement.
The identities of the end client and implementation partner remain confidential. SilwaTech does not represent itself as the direct contracting party or sole technical implementer for this engagement.
Security and Governance Focus
Protection of proprietary engineering information was fundamental to the platform.
Key areas of focus included
- Encryption of documents and embeddings in transit and at rest
- OAuth 2.0-based authentication
- Role-based access aligned with organisational responsibilities
- Document-level access controls
- Secure management of vectorised content
- Detailed logs of document access and AI interactions
- Traceable source references for generated answers
- Monitoring of unusual or unauthorised activity
- Controlled document ingestion and version management
- Regular security reviews
- Human verification of sensitive technical information
These controls improved access to engineering knowledge while maintaining protection of confidential documents and intellectual property.
Value Delivered
The engagement contributed to
- Reduced time spent responding to routine technical queries
- Faster retrieval of relevant engineering information
- Improved access to manuals, diagrams and schematics
- Contextual AI responses supported by source references
- Stronger access control for proprietary documentation
- Improved visibility of document access and usage
- More structured onboarding for new engineers
- Reduced dependence on senior specialists for routine knowledge requests
- A scalable platform supporting concurrent engineering users
- Continuous improvement based on governed user feedback
Why Silwatech
This engagement demonstrates the type of secure document-intelligence capability SilwaTech can support through its technology partner ecosystem, including
Access to specialists in enterprise document intelligence
Experience with semantic retrieval and large language models
Integration of text-based and visual-content analysis
Secure handling of proprietary and IP-sensitive information
Strong emphasis on access control, source traceability and auditability
Human-in-the-loop controls for technical knowledge retrieval
Scalable cloud-based enterprise architecture
Structured collaboration with specialised implementation partners
Selected Enterprise & Public Sector Experience
Our project experience spans government authorities, regulated enterprises, and major organisations across multiple industries.
References shown may include direct SilwaTech engagements and work delivered through our contracted partner network. Inclusion does not imply a direct contractual relationship with, or endorsement of, SilwaTech.
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