Virtual Vehicle : AI Knowledge Hub for Engineering

Europe’s largest R&D centre for virtual vehicle technology and the origin of Context64’s core knowledge graph work. Together with Virtual Vehicle, Context64AI ...

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Customer: Virtual Vehicle Research GmbH (ViF)
Role: Research Partner
Industry: Automotive R&D & Virtual Vehicle Development

Challenge

Engineering organizations generate large volumes of highly structured but fragmented knowledge across vehicle development, simulation, and validation activities.

At Virtual Vehicle, this created recurring challenges:

  • Engineering knowledge is distributed across ViF data structures, tools, and documents
  • Employees struggle to find the right knowledge when and where it is needed
  • Knowledge transfer relies heavily on individuals rather than systems
  • Expertise is lost when employees leave, change roles, or projects end

Baseline reality:

  • Knowledge bases exist but are difficult to navigate
  • Search is document- and keyword-based, not context-aware
  • Internal knowledge spreads slowly across teams and locations
  • Organizational changes lead to permanent knowledge loss

As a result, valuable engineering knowledge was underutilized and insufficiently preserved.

Solution

AI Knowledge Hub with ViF-Aware Structure

Together with Virtual Vehicle, Context64AI built an AI-driven knowledge management system that takes the ViF data structure into account as the foundation for organizing and accessing engineering knowledge.

Core approach:

  • Used ViF data structures as the semantic backbone of the system
  • Built a domain-specific knowledge graph reflecting vehicle development context
  • Introduced the ViF Bot as an AI interface for employee knowledge access
  • Enabled context-based retrieval instead of manual search

This ensured the system reasoned over engineering structure and relationships, not disconnected documents.

Result

Metric Before After Impact
Knowledge access Manual search AI-assisted, contextual Faster retrieval
Knowledge reuse Limited Organization-wide Improved utilization
Knowledge retention Person-dependent System-preserved Reduced loss
Internal knowledge spread Slow Accelerated Higher efficiency

Strategic Impact

  • Knowledge bases are improved exactly where they are needed
  • Internal knowledge spreads faster across teams and projects
  • Critical expertise is preserved beyond employee turnover
  • New concepts are validated in one of Europe’s most demanding engineering R&D environments

Research outcomes from Virtual Vehicle directly inform Context64 product features and workflows.

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Case Studies
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