SOLUTION – CLASSIFY & FIND
Classify and Find Reusable Engineering Assets
Find any requirement, model, diagram, or engineering artifact-instantly. This solution enables engineering teams to classify, search, and navigate all engineering assets across tools and projects using semantic technologies and AI. By making knowledge easily discoverable, organizations accelerate reuse, reduce duplication, and improve engineering productivity.
USE CASES
Use Cases for Classifying and Finding Reusable Engineering Assets
Integrate Engineering Tools into a Unified Search Environment
Engineering data is spread across MBSE tools, requirements systems, PLM platforms, and documents. This solution connects them into a unified environment where all assets can be indexed, classified, and searched—without replacing existing tools.
Outcome:
- Centralized access to engineering knowledge
- Cross-tool search and discovery
- Improved interoperability
Implement a Semantic Asset Repository
Create an enterprise-scale repository for requirements, models, architectures, and patterns.
Capabilities:
- Centralized storage of engineering assets
- Semantic classification based on meaning
- Advanced search across all artifacts
Outcome: Faster discovery of reusable assets and improved consistency across programs.
Enable Semantic Search Across Engineering Data
Move beyond keyword-based search to meaning-based discovery.
Capabilities:
- Natural language queries
- Concept-based search results
- Context-aware ranking of artifacts
Outcome: Find relevant engineering information—even when terminology differs.
Navigate Engineering Knowledge Through Traceability
Explore relationships between artifacts instead of isolated items.
Capabilities:
- Navigation from requirements to models, simulations, and tests
- Visualization of traceability networks
- Impact analysis through connected artifacts
Outcome: Better understanding of system structure and dependencies.
Automatically Classify and Recommend Assets with AI
Use AI to continuously organize and enrich engineering knowledge.
Capabilities:
- Automatic classification of artifacts
- Detection of related assets and patterns
- Recommendations of reusable elements
Outcome: Reduced manual effort and improved discovery of hidden knowledge.
Why Engineering Asset Classification and Discovery Matters
Typical issues include
- Engineering artifacts spread across multiple tools and repositories
- Poor organization and inconsistent classification
- Limited visibility of existing assets
- Difficulty navigating traceability relationships
Impact
- Engineers spend significant time searching for information
- Existing assets are often recreated instead of reused
- Inconsistencies increase across systems and programs
- Productivity and decision-making are negatively affected
At its core, the problem is not only access, but understanding. Without a semantic layer, engineering knowledge remains hidden even when it exists.
Common Challenges in Engineering Asset Discovery
Lack of Semantic Classification
Traditional repositories rely on manual tagging, which is inconsistent and incomplete.
Fragmented Knowledge Across Tools
Engineering data is distributed across disconnected systems with different search mechanisms.
Limited Traceability Navigation
Relationships between artifacts are difficult to explore and often underutilized.
No Intelligent Relevance Detection
Keyword-based search cannot identify semantically related or contextually relevant assets.
AI-Powered Classification and Search for Engineering Assets
This solution introduces a semantic, AI-enabled framework to classify, search, and navigate engineering knowledge across the entire tool ecosystem—without replacing existing tools.
At its core, the platform
- Builds a unified knowledge graph from all engineering artifacts
- Classifies assets based on meaning rather than structure
- Enables semantic search across tools and formats
- Connects artifacts through traceability relationships
Key capabilities
- Natural language and concept-based search
- Unified access to requirements, models, and documents
- Traceability-driven navigation of engineering knowledge
- AI-assisted classification and recommendation
Result ⇔ Engineering knowledge becomes fully accessible, navigable, and reusable.
How the Asset Discovery Process Works
Connect engineering data sources
Integrate tools, repositories, and documents.
Index and classify assets
Build a semantic representation of all artifacts.
Search and explore knowledge
Use natural language or structured queries.
Navigate through traceability
Explore relationships between engineering elements.
Access source artifacts
Retrieve information directly from original tools.
Powered by SES ENGINEERING Studio
Integration across tools
Requirements, MBSE, PLM, and document-based tool integration.
Traceability and V&V
Built-in support for traceability, V&V, and technical management.
AI-powered analysis
AI-powered analysis and recommendations for engineering artifacts.
Automated classification
Automated identification and classification of engineering data.
Advanced search
Advanced search across all engineering artifacts.
Semantic alignment
Semantic alignment through a shared knowledge base.
Interoperability
Interoperability across standards and tools.
Benefits of Engineering Asset Discovery
Rapid Access to Engineering Knowledge
Increased Reuse of Existing Assets
Unified Cross-Tool Search
Reduced Duplication of Effort
Reuse existing artifacts instead of recreating them from scratch.
Traceability-Driven Insights
Understand systems through relationships, not isolated elements.
Improved Engineering Productivity
Foundation for Knowledge-Centric Engineering
Built for Complex Systems Engineering
Aerospace & Defense
Automotive
Railway
Maritime
Energy & Environment
Telecommunications
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