Context64.ai
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Context infrastructure for engineering teams

The Context Infrastructure Platformfor Engineering Teams & AI.

Context64.ai turns fragmented engineering systems into governed context — so engineers and AI can search, reason, and act with traceability.

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PLM
PLMTEAMCENTER
TKT
TicketsJIRA · POLARION
REQ
RequirementsDOORS · JAMA
CAD
CAD / CAENX · ANSYS
ERP
ERPSAP
TQA
Test / QAqTEST · FMEA
DOC
DocsSHAREPOINT
Root CauseEvidence path · graph reasoninglive
TKT · TQA · PLM · REQ · DOC5 sources linked
ISSUEBUG-2187 · overheating after firmware update
TEST-9402SPEC-V3Pack Thermal RunawayBattery Module V3
Likely root subsystem: thermal control firmware
traced 87% confidencetraceable →
Proof in numbersSwipe
In production since2022 at major OEMs
Proven upliftUp to 700% productivity (Automotive OEM)
Graph scale~10⁸ nodes · 10⁹ edges
Time to valueFirst context surface in 6 weeks
Trusted by engineering teams
Harley-Davidson logoSiemens logoBMW logoVirtual Vehicle logoNeo4j logoIBM logoEMPOSO logoThreedy logoAirbus Defence and Space logoBMW logoKarlsruhe Institute of Technology (KIT) logoGNS Systems logoVDI Wissensforum logo
The Problem

Engineering context is trapped
where AI cannot use it.

It lives across PLM, CAD, requirements and tests — fragmented, disconnected, and unusable by AI.

Today — disconnected systems
One engineering decision

Can we approve this battery module change?

PLMTeamcenterstale version
CAD / CAENX · Ansysrev mismatch
RequirementsDOORSowner missing
TestsqTESTnot verified
TicketsJirano source link
DocumentsSharePointoutdated doc
The Solution

We turn fragmented engineering data
into usable engineering knowledge.

Context64 unifies those systems into one governed context graph — queryable by engineers and AI.

With Context64 — governed context graph
QueryCan we approve this battery module change?
Yes — SIM-47 passed · evidence linked

CONTEXT IN ACTION

From one engineering change
to a traceable decision.

Follow a product change from PLM to release. See how Context64.ai connects the relevant records across engineering systems, verifies versions and evidence, and reveals what is affected, what is missing and what must happen next — with every conclusion linked back to its source.

ILLUSTRATIVE ENGINEERING CHANGE REVIEW

The Engineering Decision Thread

Can we release Battery Module V4?

STATUS
CHANGE DETECTED

The thread plays automatically — select a step to explore it yourself

Engineering workspace showing Battery Module V4, six source-system records and the governed context graph beneath the product.Interactive diagram. Source records from PLM, requirements, CAD/CAE, test, quality and ticketing systems connect to a governed context graph of parts, requirements, simulations, tests, risks, tickets and approval gates managed by Context64.ai.CONTEXT64.AI · GOVERNED CONTEXT LAYERDCHData Context HubDCH — Data Context Hub. Builds governed, versioned and source-linked context.M4AIMemory 4 Your AIM4AI — Memory 4 Your AI. Delivers scoped and traceable context to agents and applications.CR-2187CHANGEP-4402 · Rev CPARTCR-92REQUIREMENTSIM-47 · Rev BSIMULATIONBT-9402 · Rev BTESTBF-118RISKBUG-2187TICKETG-17APPROVALUNASSIGNEDOWNERBattery Module V4BMS ControllerCell ModuleThermal SensorCooling ManifoldP-4402 · Rev CRev B → Rev CPLM · TEAMCENTERCooling ManifoldP-4402 · Rev CRev B → CREQUIREMENTS · DOORSMaximum operating temp.R-92 · v3R-92Status: ApprovedCAD / CAE · NX · ANSYSThermal SimulationSIM-47Rev BValidated for Rev BTEST · qTESTThermal CyclingT-9402Rev BLast run: Rev BQUALITY · FMEACooling manifold leakF-118S 6O 5D 5RPN 150Risk under reviewTICKETS · JIRAOverheating after fw updateBUG-2187In review

01 — A CHANGE ENTERS

Cooling manifold revised

A supplier-driven material change creates a new component revision in PLM. The change appears local, but its downstream impact is not.

CHANGE
CR-2187
PART
P-4402 · Rev B → Rev C
REASON
Supplier material substitution

One change. Multiple downstream dependencies.

02 — CONTEXT CONNECTS

Related engineering records are found across systems

Context64.ai resolves the product identity across PLM, requirements, CAD/CAE, test, quality and ticketing systems — without replacing the tools where the work happens.

INTAKE · SOURCE-AWARE
12Linked requirements
24Tests and simulations
6Affected revisions
2Approval gates

03 — EVIDENCE IS VERIFIED

Versions, ownership and evidence are checked

DCH builds the governed context graph and preserves the source, revision, owner and evidence path for every relationship.

DCH · GOVERNED CONTEXT

FINDINGS

  • SIM-47 covers Rev B, not Rev C
  • T-9402 evidence is stale for the current revision
  • Approval Gate G-17 has no assigned owner

04 — IMPACT IS ASSESSED

M4AI reasons over the relevant context

The agent receives scoped, traceable context — not a document dump — and identifies the concrete release impact.

M4AI · SCOPED REASONING

IMPACT

  • Thermal validation is required for Rev C
  • Two approval gates remain open
  • The cooling-manifold FMEA may require an update
REQUIREMENTS12 affected
VALIDATION24 tests and simulations reviewed
APPROVAL2 gates pending

05 — DECISION IS TRACED

HOLDRelease decision

Battery Module V4 is not release-ready until thermal evidence is updated for Rev C and the missing approval owner is assigned.

REASONS

Every statement links back to its source.

Human approval is required for every write-back.

Phase 1 of 5, change enters. A supplier-driven material change created Cooling Manifold revision C in PLM as change CR-2187. Status: change detected.

Your systems remain the source of record. Context64.ai adds the governed context layer above them.

SOURCE-LINKEDVERSION-AWAREPERMISSION-AWARETRACEABLE

The result: Engineers see what changed, what is affected, what evidence is missing and what must happen next.

Use cases

We help engineering teams solve the decisions that fall between systems.

Start with one high-value workflow. C64.ai connects the relevant sources, establishes the governed context and turns it into a productive Engineering AI application.

Live at OEM

Conversational FMEA knowledge assistant

An OEM engineering team asks FMEA questions in natural language — the app confirms the right entities, traverses the governed knowledge graph, and answers with full traceability.

  1. 01InputFMEA number · part number · system element · component · failure mode · engineering question
  2. 02Context64.ai tracesFunctions · failures · causes · effects · requirements · tests · mitigations · evidence · graph relationships
  3. 03OutputA traceable answer for FMEA review, investigation, or audit preparation
ResultFaster FMEA reasoning with less dependence on exact terminology and manual graph navigation
FMEAOEMKnowledge GraphTraceabilitySkill Flows
Read the OEM FMEA use case
Live at OEM

Battery Module V3 requirement update

A single requirement change can affect specifications, tests, FMEA logic, CAD assemblies, release gates, and downstream variants.

  1. 01InputRequirement revision · spec update · part change
  2. 02Context64 tracesAffected tests · FMEA entries · CAD assemblies · release gates · variant dependencies
  3. 03OutputFull impact view before approval
ResultFaster engineering decisions with fewer downstream surprises
RequirementsSpecsTestsFMEACADRelease GatesVariants
Live at OEM

Audit readiness before release

Engineering teams verify whether each requirement is connected to specifications, tests, and release evidence.

  1. 01InputRequirements · specs · test evidence · release artifacts
  2. 02Context64 checksCoverage gaps · missing evidence · stale links · incomplete traceability
  3. 03OutputClear view of what is covered and what is missing
ResultCatch gaps before audits and reduce manual evidence chasing
RequirementsCoverageEvidenceAuditTraceability
Live at OEM

Field issue traced to subsystem

A bug ticket is traced across engineering evidence to identify the most likely responsible subsystem.

  1. 01InputTicket · test result · requirement links · revision history
  2. 02Context64 followsEvidence paths across specs, components, tests, and design changes
  3. 03OutputLikely root subsystem with supporting artifacts
ResultReduce investigation time and improve confidence in corrective action
TicketsEvidence PathTestsRevisionsRoot Cause
Live at OEM

SysML architecture drift detection

Context64 identifies where architecture, requirements, and downstream engineering artifacts have drifted out of sync.

  1. 01InputSysML models · requirements · validation assets · downstream artifacts
  2. 02Context64 alignsModel elements · trace links · implementation references · validation coverage
  3. 03OutputTargeted sync view of what needs updating
ResultPrevent silent drift between architecture and implementation
SysMLMBSERequirementsValidationDrift Detection
Live at OEM

Variant-specific change assessment

A change is evaluated across product variants, markets, and production configurations to show exactly what is affected.

  1. 01InputComponent update · configuration tree · market/variant structure
  2. 02Context64 reasons overVariant dependencies · downstream impact · configuration relationships
  3. 03OutputPrecise insight into affected and unaffected variants
ResultAvoid over-scoping change decisions and reduce variant complexity risk
VariantsConfigurationMarketsProduction LineImpact

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 engineering
Proof

Built with teams working on complex products.

Co-developed and deployed with OEMs, suppliers, research organizations, and technology partners across engineering-heavy environments.

Automotive · 5 min read

German OEM builds a linked data layer for engineering systems

A linked data layer unified engineering knowledge across systems into a single contextual fabric, with a Knowledge Graph at the core.

Read case study
Technology Partner · 5 min read

Making engineering data lakes usable with IBM

IBM and Context64 turn massive engineering data lakes — vehicles, manufacturing, simulations — into usable governed context.

Read case study
Automotive · 5 min read

AI-driven test generation for automotive engineering

With Emposo, Context64 implemented TestForge — generating test cases directly from complex engineering inputs.

Read case study
Research Partner · 5 min read

Virtual Vehicle: an AI knowledge hub for engineering

Europe’s largest virtual-vehicle R&D centre and the origin of Context64’s core knowledge graph work.

Read case study
View all case studies
DCHContext engine

Data Context Hub

Connects engineering data, models the ontology, and delivers precise, governed context on demand.

PLMCADREQTEST
Governed graph
OntologyContext graphRetrievalGovernance
Explore the Data Context Hub
M4AIAgent engine

Memory 4 Your AI

Build no-code agent systems on the DCH knowledge graph — the graph is their memory, scoped and governed.

Scoped memoryBattery-V3ThermalReqs
AAgent
reasoning on graph
No-code builderGraph memoryMulti-agentAny LLM
Explore Memory 4 Your AI
In their words

Trusted by people building engineering AI.

Implementation Partner
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.
Roman BretzCTO · Emposo
Customer
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.
Engineering leadershipGerman Automotive OEM
Research Partner
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.
Research leadershipVirtual Vehicle Research GmbH
Consulting Partner
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.
Dr. Stefan WenzelManaging Director · 3DSE Management Consultants
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.

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.

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