Success stories from engineering-heavy teams.
Explore how Context64.ai helps teams connect fragmented engineering systems into governed context for search, analysis, and AI agents.
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Real engineering. Real results.
See how leading engineering organizations use Context64 to transform fragmented data into actionable, governed context.
German OEM Builds a Linked Data Layer for Engineering Systems
A large German automotive OEM unified PLM, CAD, requirements, test and supplier data into a single governed knowledge graph — cutting engineering search and rework by 60–70% and laying an AI-ready foundation.
Read case study→Making Engineering Data Lakes Usable with IBM
A semantic data layer built on watson.data and Context64AI lets engineers work on engineering scenarios, not schemas — turning underused data lakes into directly usable, AI-ready operational intelligence.
Read case study→AI-Driven Test Generation for Automotive Engineering
TestForge, built by Context64AI and Emposo, generates test cases from engineering specifications by reasoning over a knowledge graph — delivering a 700% productivity increase and 84% lower end-to-end cost.
Read case study→Context-Aware Engineering Intelligence for 3D Models
Threedy’s instant3Dhub combined with Context64AI lets engineers select a part in a 3D model and instantly retrieve the linked engineering knowledge behind it — turning visual discovery into knowledge retrieval.
Read case study→Virtual Vehicle: AI Knowledge Hub for Engineering
At Europe’s largest virtual-vehicle R&D centre, Context64AI built a domain-specific knowledge graph and the ViF Bot — shifting engineering teams from manual search to context-aware retrieval and preserving expertise across staff transitions.
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