PLMTeamcentersync
ALMPolarionsync
ERPSAPsync
CADNX · CATIAsync
Engineering Systems
PLM, ALM, ERP, CAD, requirements, simulation, documents — connected through DCH intake.
Platform · Memory 4 Your AIRuntime on DCH
Governed context becomes scoped agent intelligence.
M4AI is the runtime that runs agents on your DCH knowledge graph — not stale vector snapshots. Scoped memory, auditable reasoning and multi-agent execution, delivered into real surfaces.
The C64.ai stack live
Application SurfacesUI · MCP · REST
M4AI · Agent Runtimescoped memory · agents
DCH · Context Graphgoverned · lineage
01 — Built on DCH
Built on the Data Context Hub.
M4AI does not create isolated AI workflows. It runs on the governed context layer created by DCH.
01 · SourcesEngineering Systems
PLM · Teamcenter, ALM · Polarion, ERP · SAP, CAD · NX — connected through DCH intake.
02 · ContextDCH Context Graph
Governed graph — provenance, lineage, access control by construction.
03 · AgentsM4AI Agent Systems
Scoped memory, auditable reasoning, multi-agent execution over the graph.
04 · SurfacesApplication Surfaces
UI Builder, MCP, REST, embedded tools — capabilities ship to where work happens.
02 — Open & embeddable
Delivered through every channel.
The same M4AI capabilities ship through multiple integration channels — tap one to see how.
UI Builderin-platform
Compose agent surfaces visually — no code.
change_impact_panel · published v2.1
Same runtime, same governed graph. Only the delivery method changes.
03 — Memory
The graph is the memory.
Scopes are drawn straight from governed entities and relations. No vector DBs, no stale snapshots, no fragile RAG.
DCH knowledge graph
scopeBattery Module V3128 nodes
scopeSupplier Change46 nodes
scopeThermal Runaway73 nodes
scopeRequirements210 nodes
AgentChange Impact Systemreasoning on retrieved memory
Battery-V3SupplierThermalReqs
04 — 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.
New · live at automotive OEM
Automated agent systems for large graphs.
The M4AI Agent Harness decomposes tasks, scopes the graph and launches specialized agents in parallel to aggregate an evidence-backed result. FMEA at an OEM: from a 30M-node graph it scopes ~100k nodes, then up to 1,000 agents search in parallel.
01 · TaskA task comes in
A question, check or workflow objective starts the Harness.
"Which FMEA modes have no linked test evidence?"
02 · Graph scopeScope 100k nodes from the 30M graph
Filter a 30M-node graph down to ~100k relevant nodes — variants, requirements, tests, evidence.
30M → 100k · less noise
03 · Agent meshSpawn up to 1,000 agents
Fan the scoped context out to specialized agents that run in parallel — each scoped and observable.
divide-and-conquer · parallel
04 · Graph executionTraverse, reason, report
Agents traverse relationships, detect gaps and return findings — preserving sources and permissions.
parallel traversal · tool control
05 · Evidence outputEvidence-backed result
Aggregate findings, resolve contradictions, return a traceable result with source paths.
3 gaps · 2 tests outdated · sources attached
Auto-orchestrated agent mesh
Expensive models plan → smaller agents execute → the graph carries the intelligence.
FastCost-efficientLess token usageBetter scale
05 — In practice
What M4AI looks like in practice.
Agent capabilities land in real surfaces — over the governed DCH graph.
app.context64.ai / builderreal
Engineering Search
Natural-language retrieval across sources.
M4AI · Ranks & links results across the scoped graph.
Change Impact
Trace the blast radius of a change.
M4AI · Agents traverse requirements, tests, suppliers.
FMEA Audit
Audit failure modes for missing evidence.
M4AI · Flags modes with no linked test evidence.
06 — Any LLM · any deployment
Model-independent by design.
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
07 — In production
Agent memory, not a chatbot.
Turn your context into agent memory.
Build no-code agent systems on a governed graph — auditable, embeddable, and ready for engineering work.
M4AI · Memory 4 Your AI
Governed engineering context becomes scoped agent intelligence.
M4AI is the runtime that runs agents on your DCH knowledge graph — not stale vector snapshots. Scoped memory, auditable reasoning and multi-agent execution, delivered into the surfaces engineers already use.
Scoped memoryAuditable reasoningMulti-agent executionOpen & embeddable
The C64.ai stackruntime layer
Delivery
Application Surfaces
where engineering work happens
UI BuilderMCPRESTEmbedded
You are here
M4AI · Agent Runtime
Memory 4 Your AI
scoped memory · agents · harness
Scoped memoryAgent meshAuditable
DCH · Context Graph
Data Context Hub
governed · provenance · lineage
Knowledge graphAccess controlVersioned
Built on DCH01 / 09
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.
provenancelineageaccess
DCH Context Graph
Governed knowledge graph — provenance, lineage, access control by construction.
MOrchestratorscoped memory
Change Impact agentscoped
Requirements agentscoped
FMEA agentscoped
M4AI Agent Systems
Scoped memory, auditable reasoning, multi-agent execution — reasoning over the graph.
UIUI Builderin-platform
MCPMCPprotocol
{}REST APIhttps
SDKEmbeddeddrop-in
Application Surfaces
UI Builder, MCP, REST, embedded tools — agent capabilities ship to where work happens.
Create agent systems02 / 09
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.
app.context64.ai / builder · Complex Agent System real product
Open & embeddable03 / 09
Delivered through every channelOpen and embeddable.
The same M4AI capabilities are delivered through multiple integration channels — the in-platform UI builder, MCP, REST API, embedded tools, or native connectors. Pick a channel to see how it ships.
Integration channelsselect one
Same runtime, same governed graph. Only the delivery method changes.
M4AI · UI Builderpublished · v2.1
Components
Agent
Table
Chart
Filter
Change Impactagent panel
Requirements traced14
Simulation runs24
Approval gates2
compose: drag agents → wire → publish
Memory04 / 09
The graph is the memoryThe graph is the memory.
Connect agents to your DCH knowledge graph. The graph is their shared long-term memory — scopes are drawn straight from governed entities and relations.
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 → AgentIDLE
DCH knowledge graph
IMPLEMENTSTESTED_BYSUPPLIED_BYREQFMEARELEASEPARTSUPPLIERDOCTESTSIM
Scoped memories
scopeBattery Module V3128 nodes
scopeSupplier Change46 nodes
scopeThermal Runaway73 nodes
scopeRequirements210 nodes
Agent system
Agentwaiting
Change Impact System
waiting for context
Battery-V3SupplierThermalReqs
✓REQ-4471 → PART-92 · source-linked
✓PART-92 → 24 test runs · evidenced
✓Supplier change · 2 approvals open
Collaborate05 / 09
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 systemAgent Harness06 / 09
Automated agent systemsC64.AI-Stack✓Data Context Hub✓M4AI Agent Builder3M4AI Agent Harness
New C64.ai product · Live at automotive OEM
Automated agent systems for large engineering graphs.
M4AI Agent Harness decomposes engineering tasks, scopes the relevant context and launches specialized agents in parallel to aggregate an evidence-backed result — with permissions, controls and model independence. Live example, FMEA at an automotive OEM: from a 30M-node graph the Harness scopes ~100k relevant nodes, then up to 1,000 agents search them in parallel and return evidence-backed answers.
Graph-native execution — not just agent workflow orchestration.
Harness orchestrationup to 1,000 agents
Auto-orchestrated agent mesh
Expensive models plan → smaller agents execute → the graph carries the intelligence.
Divide and conquer: frontier models understand the ontology, the user and the problem — then hand detailed, scoped work to smaller models as agent meshes. Those execute and report back.
FastCost-efficientLess token usageLess frontier dependencyBetter scale
In practice07 / 09
Real application surfacesWhat M4AI looks like in practice.
Agent capabilities land in real surfaces — from the M4AI builder to the surfaces engineers use daily. Each one runs on the M4AI runtime, over the governed DCH graph.
app.context64.ai / builder
M4AI Builderreal screenshot
SurfaceCompose multi-agent systems on a canvas — no code.
M4AI contributesAgents, memories, tools, provider config and versioning.
DCH providesEvery memory is a scoped region of the DCH graph.
Any LLM08 / 09
Any deploymentAny LLM. Any deployment.
Model-independent by design — the Harness plans with frontier models and executes with smaller ones. Run local, on-prem, private or public cloud.
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 by Context64.ai09 / 09
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.