Change Impact
Predict the blast radius of every change.
Change a requirement, spec or part — see every affected test, FMEA, CAD assembly and release in seconds.
Context infrastructure for engineering teams
Creating the Context Infrastructure for Engineers.
Context64.ai turns fragmented engineering systems into governed context — so engineers and AI can search, reason, and act with traceability.
Context flowlive
PLMTeamcenterCADNX · AnsysERPSAPJIRAticketsREQDOORSTESTqTEST
DCHData Context Hubgoverned context graph
M4AIMemory 4 Your AIgraph as agent memory
FMEA Auditlive
Re-evaluate FMEA on the motor controller line.
24linked tests
2approval gates
source-linkedversion-aware
03 · Why Context Matters
From scattered engineering data to usable context.
Engineering knowledge is spread across PLM, CAD, ERP, requirements, test systems, documents, and supplier data. Context64 turns that fragmented landscape into a governed context graph that engineers and AI systems can search, reason over, and act on.
Company BrainLLM-sovereign by design
Your context becomes a durable company brain — portable across any model, no lock-in.
80%More AI accuracy
Governed, graph-scoped context instead of vague retrieval over document dumps.
Up to 87%Fewer tokens
Exactly tailored context per task — consistent quality at a fraction of the operational cost.
03 — The stack
One platform. Two engines. Many surfaces.
Intake → DCH → M4AI → Delivery — with C64 Studio as the workbench across the stack. DCH builds context, M4AI reasons over it, Studio helps build the applications and workflows that expose it wherever engineering work happens.
01Intake Layer
Connect any engineering source
Connect the systems, repositories, and workflows where engineering work already happens — PLM, CAD, requirements, tests, tickets, and documents.
ConnectorsIntake agentsWorkers
Explore Intake Layer IntakeDCHM4AIDelivery
Application workbench for creating tools, workflows, and engineering surfaces across the stack.
Data-ForgeKnowledge-ForgeTool-ForgeAgent-ForgeApp-Forge
Explore C64 Studio 04 — Application surfaces
Build any engineering application on the Context Layer.
Use what we’ve prebuilt. Or compose your own using REST API, MCP, Studio Apps and embedded tools— all on the same Context Graph.
Requirements Coverage
Every requirement, traced and evidenced.
Continuously map requirements to specs, tests and release evidence. Catch coverage gaps before audits do.
Root Cause Reasoning
From bug ticket to root subsystem.
Trace field issues, test failures and tickets back to the responsible spec, component or design change — across your full engineering graph.
Living FMEA
FMEAs that stay alive.
Validate FMEAs against real failure data, missing requirements and recent design changes. Flag gaps continuously.
MBSE Change Sync
Keep SysML models in lockstep with reality.
Continuously align SysML models, requirements and downstream artifacts. No more silent drift between architecture and implementation.
Variant Intelligence
Reason across every product configuration.
Understand which change affects which variant, market or production line. Reason across complex configuration trees in real time.
Build Your Own
Or let us build it with you.
Have a workflow that doesn’t fit a template? Compose it with Studio Apps, MCP and REST APIs — or partner with us to ship it.
Talk to engineeringSee it in action
One Jira issue becomes source-linked context, a graph path, and multiple usable application outputs.
Source · Jira
HighBUG-2187battery module overheating
DCH context graph
FMEA Auditmotor controller line
RPNs updated31
high-risk modes4
actions overdue9
evidenceauto-linked
05 — Proof
Built with teams working on complex products.
Co-developed and deployed with OEMs, suppliers, research organizations, and technology partners across engineering-heavy environments.
DCH
Data Context Hub
Connects engineering data, models the ontology, builds the context graph, and delivers precise, governed context on demand.
OntologyContext graphRetrievalVersioningGovernance
Explore the Data Context Hub M4AI
Memory 4 Your AI
Build no-code agent systems on top of the DCH knowledge graph — the graph is their memory, scoped and governed.
No-code builderGraph memoryMulti-agentEmbeddableAny LLM
Explore Memory 4 Your AI 06 — In their words
Trusted by people building engineering AI.
08 — FAQ
Questions teams ask before building on context.
A context graph connects engineering entities, relationships, versions, ownership, evidence, and dependencies across systems — modeling how engineering work actually relates: requirements to tests, components to changes, failures to mitigations.
RAG retrieves fragments. Context64.ai builds a governed context layer — agents and applications reason over entities, relationships, lineage, permissions, versions, and evidence, not only text chunks.
Data Context Hub builds and governs the context layer. Memory 4 Your AI uses that graph as memory for agent systems, letting teams build agents that reason over governed engineering context.
No. Context64.ai sits above existing systems and turns their data into a connected context layer. Teams keep using their current PLM, CAD, ALM, ERP, ticketing, document, and test systems.
Yes. It supports cloud, private cloud, on-prem, and controlled deployment models — including EU-sovereign and air-gapped variants where required.
Context Infrastructure for Engineering TeamsCreating the
Creating the
Context Infrastructure
for Engineers.
Context64 AI turns fragmented engineering systems into governed context, so engineers and AI systems can search, reason, and act with traceability.
C64.AI · Stack
DCH
Data Context HubIntake → Context
M4AI
Memory 4 Your AIDelivery → Apps
PLM
PLMTEAMCENTER
TKT
TicketsJIRA · POLARION
REQ
RequirementsDOORS · JAMA
CAD
CAD / CAENX · ANSYS
ERP
ERPSAP
TQA
Test / QAqTEST · FMEA
DOC
DocsSHAREPOINT
causality · defect 48274 sources linked
5traced
Evidence paths3ranked
Likely causes1field action
Affected configuration87%high
Confidence source-linked version-aware context-completetraceable →
In production since
2022 at major OEMs
Proven uplift
Up to 700% productivity (Automotive OEM)
Graph scale
~10⁸ nodes · 10⁹ edges
Time to value
First context surface in 6 weeks
One engineering decision
Can we approve this battery module change?
PLMTeamcenterstale version
CAD / CAENX · Ansysrev mismatch
RequirementsDOORSowner missing
TestsqTESTnot verified
SimulationAnsysoutdated
Jiraticketsno source link
DocumentsSharePointoutdated doc
Approvalsgatespending
02 · The Problem
Engineering context is trapped where AI cannot use it.
Engineering teams work across PLM, CAD, requirements, tests, tickets, documents, and approvals. The relationships matter most — but they are rarely available as usable context.
The information exists — but it's fragmented across systems, with no shared version, owner, or evidence. So the decision waits.
03 · Why Context Matters
From scattered engineering data to usable context.
Engineering knowledge is spread across PLM, CAD, ERP, requirements, test systems, documents, and supplier data. Context64 turns that fragmented landscape into a governed context graph that engineers and AI systems can search, reason over, and act on.
Company BrainLLM-sovereign by design
Your context becomes a durable company brain — portable across any model, no lock-in.
80%More AI accuracy
Governed, graph-scoped context instead of vague retrieval over document dumps.
Up to 87%Fewer tokens
Exactly tailored context per task — consistent quality at a fraction of the operational cost.
04 · Proof
Built with engineering teams working on complex products.
Co-developed with leading OEMs, suppliers, research organizations, and technology partners across engineering-heavy environments.
German OEM Builds a Linked Data Layer for Engineering Systems
A German OEM implemented a linked data layer that unified engineering knowledge across systems into a single contextual fabric, with a Knowledge Graph at the core.
Linked data layer for engineering systems
Read case study →Making Engineering Data Lakes Usable with IBM
Large engineering-driven enterprises generate massive volumes of operational and product data across vehicles, manufacturing systems, and simulations. IBM and Context64 make engineering data lakes usable.
Engineering Data Lake → Usable Context
Read case study →AI-Driven Test Generation for Automotive Engineering
Together with Emposo, Context64AI implemented TestForge — an AI-driven system that generates test cases directly from complex engineering inputs.
AI-driven test generation
Read case study →Context-Aware Engineering Intelligence for 3D Models
Engineering and service teams work with complex 3D assemblies made of thousands of parts. Context-aware engineering intelligence connects 3D models to the surrounding knowledge graph.
3D models + knowledge graph retrieval
Read case study →Virtual Vehicle: AI Knowledge Hub for Engineering
Virtual Vehicle is Europe’s largest R&D centre for virtual vehicle technology and the origin of Context64’s core knowledge graph work. Together they built an AI Knowledge Hub for engineering.
AI knowledge hub for engineering
Read case study →05 · Our StackOne platform. Two engines.
One platform. Two engines.
Many engineering surfaces.
DCH makes organizational context operable. M4AI turns scoped context into intelligence. Studio helps teams build the applications, workflows, and tools that expose that intelligence wherever engineering work happens.
Context64 · PlatformExplore Intake Explore DCH Explore M4AI Explore surfaces
01
Enterprise Systems & Intake
Systems · repositories · workflows
Connect systems, repositories, and workflows where engineering work already happens.
Enterprise systems
TeamcenterSAPJiraConfluenceGitHubGitLabSharePointDOORSJamaPolarionSiemens NXCATIAAnsysSnowflakeDatabricksAWS S3Azure Data LakeBigQuery+ more
Intake infrastructure
AGIntake AgentsWKWorkersWFWorkflows
02
DCH
Data Context Hub
Build and govern the context graph that connects engineering entities, relationships, lineage, and permissions.
PLMREQTSTCAD
context retrieved
requirements12 linked
tests24 evidenced
lineagev3 → v4
gates2 pending
IntakeOntologyContext GraphRetrievalVersioningGovernance
03
M4AI
Memory 4 Your AI
Turn scoped context into agent memory, reasoning, and multi-agent workflows.
Change Analyst
Requirement Agent
Test Agent
Approval Agent
graph memory · scoped
Battery Module V3Supplier ChangeEU Fleet IssueThermal Runaway
decision-ready
requirements12 linked
tests24 evidenced
gates2 pending
04
Delivery Surfaces
Outputs & interfaces
Expose capabilities through custom UIs, MCP, REST APIs, embedded tools, Studio apps, and workflows.
Use the context layer anywhere engineering work happens.
Custom UIMCPREST APIEmbedded ToolsStudio AppsWorkflowsEngineering DashboardsNative Integrations
C64 Studio · application workbench
C64 Studio
Application workbench for creating tools, workflows, and engineering surfaces across the stack.
Data-ForgeKnowledge-ForgeTool-ForgeAgent-ForgeApp-Forge
Explore Studio ✓Change Impact Explorerpublished
✓FMEA Audit surfacegenerated
✓Requirements coverage appversioned
GeneratedPublishedVersionedContext-linkedMCP / API ready
07 · Application Surfaces
Build any engineering application on the Context Layer.
Use what we’ve prebuilt. Or compose your own using REST API, MCP, Studio Apps and embedded tools— all running on the same Context Graph.
Change Impact
Predict the blast radius of every change.
Change a requirement, spec or part — see every affected test, FMEA, CAD assembly and release in seconds.
Requirements Coverage
Every requirement, traced and evidenced.
Continuously map requirements to specs, tests and release evidence. Catch coverage gaps before audits do.
Root Cause Reasoning
From bug ticket to root subsystem.
Trace field issues, test failures and tickets back to the responsible spec, component or design change — across your full engineering graph.
Living FMEA
FMEAs that stay alive.
Validate FMEAs against real failure data, missing requirements and recent design changes. Flag gaps continuously.
MBSE Change Sync
Keep SysML models in lockstep with reality.
Continuously align SysML models, requirements and downstream artifacts. No more silent drift between architecture and implementation.
Variant Intelligence
Reason across every product configuration.
Understand which change affects which variant, market or production line. Reason across complex configuration trees in real time.
Build Your Own
Or let us build it with you.
Have a workflow that doesn’t fit a template? Compose it with Studio Apps, MCP and REST APIs — or partner with us to ship it.
See it in action
One Jira issue becomes source-linked context, a graph path, and multiple usable application outputs.
01 · Source
Jira issue stream
BUG-2187Battery module overheating after firmware updateHigh
BUG-2156Coolant manifold weld voids — EU pilot batchHigh
BUG-2104Cell imbalance > 80 mV across packMedium
BUG-2091BMS calibration drift, lots ≥ 24-W18Medium
BUG-2065Service harness chafe at mount pointLow
BUG-2038Busbar fatigue under HV cyclingMedium
BUG-2187 · active
Battery module overheating after firmware update
02 · Context graph
DCH context · BUG-2187
graph · live
03 · Surface
Application output
Likely root cause path
BUG-2187 · 87% confidencesubsystemBMS · firmware path
similarBUG-1844 · resolved
triggerlot 24-W18 calibration
evidence5 traces · 3 work orders
06 · Testimonials
Trusted by people building the future of engineering AI.
Perspectives from customers, partners, research collaborators, and advisors helping shape context-aware engineering AI.
Implementation PartnerEMPOSO
“
The context problem of today’s LLMs is not a usage error — it is a systemic problem. Context64 does not solve it with ever-larger models, more tokens, and more agents, but the other way around: through precise control of the context the models work with. Instead of letting the model puzzle over a vague, enormous context, the platform delivers exactly tailored context for every task. The result: consistent quality at a fraction of the operational cost.
CustomerGerman Automotive OEM
“
From a single part selection in the PDM system, Context64 pulls the full engineering chain across Teamcenter, DOORS, JIRA and SAP — down to the line in all production facilities.
Research PartnerVirtual Vehicle
“
Decades of research only create value when engineers can find and apply them. With Context64, our project knowledge becomes a living graph that researchers and vehicle programs can actually query.
Consulting Partner3DSE
“
Together with Context64.ai, we bring AI to where it truly matters: into the daily work of development organizations. Less PowerPoint, more measurable impact — faster, scalable, and sustainable.
CustomersPartnersResearchConsulting
Questions teams ask before building on context.
Short answers to the questions engineering, data, and AI teams usually ask when evaluating Context64.ai.
A context graph connects engineering entities, relationships, versions, ownership, evidence, and dependencies across systems. Instead of treating data as isolated files or records, it models how engineering work actually relates: requirements to tests, components to changes, failures to mitigations, documents to decisions, and owners to workflows.
Still have questions?
Talk to the team. We'll walk you through how Context64.ai fits your engineering environment.
Research it yourself
Let AI assess your engineering context strategy.
Ask your assistant of choice how Context64.ai turns fragmented engineering systems into a governed context layer — connected, versioned, and traceable across design, engineering, production, and service organizations.