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The Context Layer
for your Engineering AI

Your engineers find answers across 20+ disconnected systems. Your AI doesn't.
We build the context layer that supercharges your engineering teams and their AI.
Co-developed with two of Germany's leading OEMs and a Tier-1 supplier.

The C64AI Stack is built for the entire product lifecycle and powers AI-driven engineering decisions in the world's most critical industrial enterprises across automotive, aerospace, defense and heavy machinery.

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Engineering
Context Layer

100+ data sources. One Knowledge Graph.
We build any application on top.
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Quality & Test Engineering

From quality bottleneck to release accelerator. 700% productivity gain in test engineering.
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Change & Variance Management

From warranty to homologation - every change traced across the lifecycle.
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Autonomous
Operations

Agentic operations across the enterprise. From insight to action - without dashboards.
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EMPSON logo with the tagline 'Tomorrow. Created Today.' below it.
HAYS company logo in bold uppercase letters.
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The C64AI Platform

Intake every engineering datatype

‍
PLM, ERP, CAD and 100+ engineering systems connected to one secure intake layer. Over a billion data points. Your existing tools stay where they are.

Modeled by your engineers - no code


Engineers model the domain ontology directly - entities, relationships, business rules. No code, no graph DB experts. Defined by the people who understand the data.

Agents reason on the Context Graph


The ontology fills with real cross-system data - a living, governed context graph. Agents reason on it to produce structured outputs. Production-grade, with full traceability.

Deliver real outcomes - sovereign & portable


Build custom UIs on top. Expose context via MCP, REST API, or existing workflows. EU-based, on-premise or cloud, LLM-agnostic - your ontology stays portable across models.
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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.

Why C64AI?

Dimension
What It Stores
What It Misses
Best For
Limitation
European Sovereignty
C64.AI Stack
Engineering ontology +  relationships + reasoning agents
Complete
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
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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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