The C64AI - Platform

1 · Connect every engineering source


Pull from PLM, ALM, ERP, PDM, CAD, FMEA, requirements and documents. Data Intake Agents handle structured and unstructured sources - on-premise or cloud, no custom pipelines. All your data, in one secure intake layer.

2 · Model your ontology - no-code, by your team


Your engineers model the engineering reality directly on the Data Context Hub. No code. No graph DB experts. The output: a living, governed knowledge graph - queryable in real time.The graph is the context layer.

3 · Build reasoning agents on the context layer


With Memory for your AI (M4AI), your team builds agents that reason on real cross-system context - not retrieval, not guesses - and produce structured outputs, not chat answers. Model-agnostic. Explainable. Production-grade.

4 · Deliver outcomes - UI, MCP, API, or in your tools


Build a custom UI on top. Expose context via Model Context Protocol (MCP). Integrate via REST API into existing tools. Or run the ready-made workflows we ship. One context layer, many apps - expand across the same ontology.
Diagram showing AI-powered application layers connecting analytics & workflow, automations, and system integration to factory, product, sales, and R&D sectors.

What makes engineering AI actually work

Enterprises don't have a data problem - they have a meaning and execution problem.

What is missing
Why it Matters
How C64AI Solves It
Domain Context
AI needs to understand YOUR business concepts, not just get generic language
Ontologies encode your domain: parts, requirements, tests, suppliers, regulations
Structure
Relationships between data points matter as much as the data itself
Knowledge Graph preserves hierarchies, dependencies, relationships
Execution
Retrieval isn’t enough - insights must reach decision points and trigger action
Production-ready agents the reason, retrieve AND act
“It’s not a model problem - it’s a context problem. Better models won’t fix bad context. We build the context layer.”

How It Works

Workflow diagram showing data intake from ERP, PDM, CRM, and PLM systems into Data Intake Agents, feeding a Knowledge Graph, then deploying model-agnostic agent systems that extract insights and run workflows for analytics, automation, and system integration, ultimately scaling impact.
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The C64AI Stack

Memory 4 Your AI®

Memory for AI (M4AI®) is where reasoning agents are built on the knowledge graph. Engineering agents operate on real cross-system context - not retrieval, not guesses - and produce structured outputs ready for production workflows. Model-agnostic, explainable, audit-ready.
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data context hub®

Data Context Hub (DCH®) is where the context layer is built. Engineers model their domain directly in DCH - no code, no graph DB experts - and the system continuously synchronizes data from PLM, ALM, ERP, CAD and other engineering sources into a living, governed knowledge graph.
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Memory 4 Your AI
Memory 4 your AI® (M4AI®) is an industrial agent workbench that extends  Data Context Hub®. Get a low-code industrial agent builder that enables you to use Gen AI to carry out more complex operations with greater accuracy, including workflow automation and decision-making support, and accelerate efficiencies that can generate tens of millions of dollars in business impact.
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data context hub
An open, secure industrial data management and integration  platform that enables quick deployment of a contextualized data foundation for rapid scaling of digital solutions. Take advantage of rapid ability to create complex knowledge graphs and build your own solution to solve 100s of business-critical challenges and redefine operational efficiency.
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Powerful AI Models Aren't Enough

The C64 Stack is a modular system that turns
disconnected engineering and business data
into actionable intelligence.

It starts with the Data Context Hub (DCH), which integrates structured, semi-structured, and unstructured data from across your organization into a unified knowledge graph.

Built on this graph, Memory-for-Your-AI (M4AI) deploys memory-capable agents that learn from feedback and keep context over time—so every query, analysis, or workflow becomes smarter and more accurate with each interaction.

This graph captures relationships between parts, processes, documents, tests, and more—providing the missing context traditional systems can’t deliver.

Diagram showing a multi-layer AI system architecture with layers labeled Data Context Hub, Memory for AI, Open Source and Commercial Models, and AI-Powered Application, connected by flow lines and icons representing data and AI components.

Technical Architecture

Diagram showing a data architecture workflow with layers including data intake agents, graph builder services, M4AI agent API, databases, and gateway connecting to analytics, automations, and system integration.