Deploy context-aware AI agents that understand relationships and workflows, working together to solve complex business challenges.
Open Architecture
On-premise, cloud, or hybrid - your data, your choice, no vendor lock-in. Complete flexibility for enterprise deployment.
Unified Context Platform
Connect all your data sources into a unified knowledge graph that AI can understand and leverage.
Why C64AI?
Dimension
Structure Preservation
Engineering-Grade
Production Agents
Time to Value
LLM Agnostic
European Sovereignty
C64AI
Full hierarchy & relationships
Built for PLM, ERP, CAD, est data
Execute workflows, not just retreive
Weeks with Vertical Kits
Any model, swap anytime
Austrian, GDPR-aligned
RAG Platforms
Flattened to vectors
Document-focused
Retreival only
Months of tuning
Often locked in
Mostly US-based
Data Catalogs
Metadata only
Schema-focused
No agents
Months of setup
N/A
Varies
Custom Development
Depends on build
Depends on build
Custom effort
12-18 months
Depends on build
Depends on hosting
Powerful AI Models Aren't Enough
Today's Al can write code and generate text - but 95% of enterprise Al pilots never deliver measurable business impact. The problem isn't the models, it's the learning gap: most systems don't retain feedback, adapt to workflows, or integrate with day-to-day operations.
Context engineering is how you cross this "GenAl Divide" - embedding memory, feedback loops, and deep process understanding so Al keeps getting smarter and delivers sustained P&L results.
95%
Of enterprise AI initiatives stall before production because tools don’t learn or fit real workflows.
The GenAI Divide STATE OF AI IN BUSINESS 2025, MIT NANDA, July 2025
2X
Higher success rate when companies partner with vendors to build memory-capable systems that keep improving with use.
ROI
Real impact comes when AI learns from feedback and connects siloed data through memory-capable knowledge graphs.
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