Why engineering AI doesn't ship.

Most AI at industrial enterprises stalls - not on the model, but on missing business context. C64.AI is the platform that closes that gap.

Generic Copilots

Fine-tuning /Training

Text-to-SQL /Query AI

RAG /AI Search

“Add ChatGPT/ Copilots to workflows”

“Train the model on your data”

“Ask question, get answers from your database”

“Search your docs with AI”

Where they fall short

No domain knowledge: doesn’t understand YOUR business

Can’t reason over your data

No action

Hallucinations

Expensive

Frozen: model can’t adapt to new data

No traceability

Vendor lock-in

Schema blindness

Single-Source: works on ONE database, not 10+ systems

Returns tables, not decisions

No relationships: can't reason across systems

Loses structure: chunks break BOMs and FMEAs

Stale: no live sync to sources

Documents only: ignores structured data

“Each approach solves a piece - search, learning, querying, assistance. None delivers connected context that enables AI action.”

Engineering AI before and after C64.AI

"From fragmented engineering systems to one queryable context layer."

Diagram showing disconnected factory, product, sales, and R&D sections around a central confused person icon with arrows and question marks.

BEFORE (Without C64AI)

Siloed data - Information trapped in disconnected systems

Manual search - Hours spent finding the right document

Slow decisions - Weeks to gather context for critical choices

AI demos that don’t ship - Pilots that never reach production

Lost expertise - Knowledge walks out the door with employees

Rework and errors - Missing context leads to mistakes

Person interacting with a futuristic data interface labeled Data Context Hub connected to factory, product, sales, and R&D icons below.

AFTER (With C64AI)

Connected knowledge - One graph linking all your data

Instant context - The right information in seconds

Fast decisions - Context-rich insights on demand

AI systems that execute - Agents in production, delivering value

Preserved expertise - Organizational knowledge captured in the graph

First-time-right - Full context prevents errors

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