Your engineers know how to find answers across 20+ systems. Your AI doesn't. We build the context layer that that supercharges your engineering teams and their AI.
The C64AI Stack powers live, AI-driven engineering decisions in the world's most critical industrial enterprises — across automotive, aerospace, defense and heavy machinery. Built for the entire product lifecycle.
PLM, ERP, CAD and 100+ engineering systems unified into one knowledge graph. Your existing tools stay where they are.
AI that knows your business
Grounded in your data, processes and rules. Reliable answers across engineering - no generic guesses, no hallucinations.
Agents that ship
Beyond pilots that stall. Workflows executed across systems, in production, with full traceability.
Sovereign & portable
EU-based, on-premise or private cloud, LLM-agnostic. Your engineering ontology stays portable across model generations.
The Context64AI Advantage
See what's affected - instantly. Change impact analysis in minutes, not weeks.
Change a requirement, swap a supplier, find a defect - and instantly see every test, document, product and regulation affected. Manual impact analyses that took weeks happen in minutes.
Catch the €500 issue before it costs €5M. Problems found at design - not after launch.
Every engineering decision grounded in real context: supplier constraints, regulations, prior failures. Catch issues while they're still cheap to fix not after the recall or audit.
From engineers' heads to your enterprise memory. Decades of engineering decisions stay queryable.
When a senior engineer retires, their judgment usually goes with them. With c64.AI, every trade-off and lesson stays accessible - to next-generation engineers and every AI agent.
Engineering teams running production AI on live cross-system data
Weeks with Vertical Kits
Austrian, GDPR-aligned
RAG / Enterprise AI( ChatGPT,Copilot)
Document chunks, vector embeddings
Doesn't understand engineering structure or relationships
General knowledge retrieval
Hallucinates on engineering specifics
Mostly US-based
PLM(Teamcenter, Aras, Windchill)
BOMs, revisions, approval status
No cross-system reasoning. Not AI-ready.
Configuration management
Captures the what, not the why
Varies
ALM / RE(IBM DOORS, Codebeamer)
Requirements, tests, defects, change traceability
No semantic reasoning. Linear, not graph-based.
Regulated requirements lifecycle
Siloed traces, no cross-domain context
Depends on hosting
Powerful AI Models Aren't Enough
AI models are everywhere. Structured data, the ground truth of AI is not. That's why 95% of enterprise AI never reaches production. The bottleneck isn't the model. It's the missing context.
Context engineering closes this gap. Not better prompts, better context: your data, your business logic, your workflows, connected into a layer AI can reason with and act on.
$120B
Of enterprise AI initiatives stall because they don’t connect to real workflows and structured data.
- Jensen Huang, NVIDIA GTC 2026
7x
Productivity increase in test case generation using knowledge graph-driven, workflow-aligned agents
65%
Using a context-driven approach, search and rework in a German OEM environment were reduced by 65%, unlocking millions annually.
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