Engineering Systems
PLM, ALM, ERP, CAD, requirements, simulation, documents — connected through DCH intake.
Platform · Memory 4 Your AI
Memory 4 Your AI turns context into agent memory.
Build no-code agent systems on the DCH knowledge graph. The graph is their memory — scoped, governed, and auditable. Open, embeddable, any LLM, any deployment.
Graph → memory → agents → output live
DCH context graphgoverned · versioned
Scoped memorytask-scoped slices
Agent teammulti-agent · hand-off
Structured outputevidence-backed
01 — Why it’s different
Agent memory, not a chatbot.
02 — Collaborate
Not one chatbot. A coordinated team.
01Analyse · scoped:Battery-V3Change Request AnalyserDone
02Trace · scoped:R-92Requirements AgentDone
03Verify · scoped:Sim-47Test Evidence AgentRunning
04Audit · scoped:ISO-26262Compliance AgentWaiting
05Approve · scoped:F-118FMEA AgentNeeds approval
▼ Final output
Change Impact: torque spec R-92 → revision C — evidence-backed and ready for approval.
03 — Open & embeddable
Ships to where work already happens.
Integration channels
MCPREST APIExisting toolsUI BuilderEmbeddable
Any LLM
OpenAI · GPTAnthropic · ClaudeGoogle · GeminiMistralMeta · LlamaOpen-source
Any deployment
Public cloud
Provider of your choice
Private cloud
EU sovereignty options
On-premise
Inside the customer perimeter
Local / air-gapped
For sovereign engineering envs
04 — Built on DCH
The platform flow.
01 · SourcesEngineering Systems
PLM, ALM, ERP, CAD, requirements, simulation, documents.
02 · ContextDCH Context Graph
Governed graph — provenance, lineage, access control by construction.
03 · AgentsM4AI Agent Systems
No-code agent systems reasoning over the graph — scoped, multi-agent.
04 · SurfacesApplication Surfaces
UI Builder, MCP, REST, embedded tools — capabilities ship to where work happens.
Turn your context into agent memory.
Build no-code agent systems on a governed graph — auditable, embeddable, and ready for engineering work.
Create agent systems01 / 07
No codeCompose agent systems. No code.
Drag-and-drop for engineering teams. From a single agent to coordinated multi-agent systems: Change Impact, FMEA, Requirements, Compliance — your domain, your agents.
Memory02 / 07
The graph is the memoryThe graph is the memory.
Connect agents to your DCH knowledge graph. The graph is their shared long-term memory.
Agents reason on real engineering context, hand off tasks across systems, and keep every step auditable. No vector DBs, no stale snapshots, no fragile RAG.
- ✕No vector drift between embeddings and source records
- ✕No stale snapshots — memory tracks the graph in real time
- ✕No fragile RAG that breaks under engineering complexity
- ✓Provenance & revision history for every retrieved fact
DCH Graph → Scoped Memories → AgentRETRIEVING 0/4
DCH knowledge graph
REQPARTTEST
Scoped memories
scopeBattery Module V3
scopeSupplier Change
scopeThermal Runaway
scopeRequirements
Agent system
Agent
Change Impact System
waiting for context
Battery-V3SupplierThermalReqs
Collaborate03 / 07
Not one chatbot — a coordinated teamNot one chatbot. A coordinated agent team.
Specialized agents access different parts of the graph and hand off tasks to solve complex engineering problems together.
Change Request AnalyserRunningChange · R-92
Requirements AgentWaitingRequirements
Test Evidence AgentWaitingTest runs
Compliance AgentWaitingApproval gates
FMEA AgentWaitingRisk
▼ Assembling…
Change impact for R-92 → revision C: 14 requirements traced, 24 simulation runs evidenced, 2 approval gates identified — full audit trail attached.
total 7.3s · 5 agents · 1 systemOpen04 / 07
Embeddable · era of agentsOpen and embeddable.
Ship agent capabilities to your team through the in-platform UI builder, MCP, REST API, or embed directly into existing tools. One stack, many apps.
M4AI · Agent Runtime · embeddable on every surface
M4AI Runtime
Agent Execution
powered by DCH graph
ESEngineering Search
CIChange Impact
RCRequirements Coverage
RARoot Cause Analysis
FAFMEA Audit
CWCustom Workspace
Integration channels
Example · UI Builder
surface: "change_impact_panel"
system: "change_impact_system"
compose: drag agents → wire → publish
state: published · v2.1
Any LLM05 / 07
Any deploymentAny LLM. Any deployment.
Compatible with GPT, Claude, Gemini and open-source models. Run local, on-prem, private cloud, or public cloud — you choose the provider and deployment model.
LLM providers
Bring your own modelGPT
OpenAI · GPT4o · 4.1 · 5CL
Anthropic · ClaudeSonnet · OpusGM
Google · GeminiPro · FlashMS
MistralLarge · SmallLL
Meta · Llama3.1 · 3.3OS
Open-sourceQwen · DeepSeek · …Deployment
You choosePublic cloud
Provider of your choice
Private cloud
EU sovereignty options
On-premise
Inside the customer perimeter
Local / air-gapped
For sovereign engineering envs
Built on DCH06 / 07
The platform flowBuilt on the Data Context Hub.
M4AI does not create isolated AI workflows. It runs on the governed context layer created by DCH — and ships through application surfaces engineers actually use.
DCH Context Graph
Governed knowledge graph — provenance, lineage, access control by construction.
M4AI Agent Systems
No-code agent systems reasoning over the graph — auditable, scoped, multi-agent.
Application Surfaces
UI Builder, MCP, REST, embedded tools — agent capabilities ship to where work happens.
Built by Context64.ai07 / 07
In production · at OEMsIn production. At OEMs. Not a roadmap.
M4AI agents have been reasoning on real engineering data at scale for years — built on top of the Data Context Hub. Build your engineering AI team where context already lives.
Persistent memory
Built on the DCH knowledge graph. No vector drift. No loss of context across sessions.
Auditable reasoning
Every step traceable and explainable. Built for engineering governance and safety reviews.
Multi-agent workflows
Specialized agents collaborate and hand off work across engineering domains — coordinated, not chatty.
OEM-grade context
Real engineering data — systems, models, requirements, tests, processes. Not generic enterprise search.
M4AI · Memory 4 Your AI
Build your engineering AI team.
Turn your DCH knowledge graph into a living agent system — governed, embeddable, and ready for real engineering work.