Connect any engineering source into context.
Bring data from PLM, ALM, ERP, CAD, requirements, FMEA, documents, tickets, repositories, files, databases, and data lakes into one governed context layer.
Intake is not just extraction — Context64.ai preserves source meaning, relationships, lineage, ownership, and engineering semantics so DCH can build a living context graph.
Connect → extract → map → govern.
Authenticate, configure access, and define source scope — read-only where required.
Read data, metadata, structure, documents, and relationships from the source.
Map source objects into engineering entities, relationships, and ontology concepts.
Preserve lineage, access rules, versioning, and ownership — DCH-ready.
Make context available to DCH, M4AI agents, and application surfaces.
Engineering, enterprise, work, and data platforms.
Source-aware ingestion.
Source-aware connectors that read each system on its own terms — schemas, documents, links, owners.
Map source objects into engineering entities, relationships, and attributes against your ontology.
Orchestrate, schedule, validate, and govern ingestion — incremental, streaming, or read-only.
Example intake agents
Lineage, ownership, relationships, meaning.
No. Context64.ai supports standard source patterns and custom integration through intake agents, workers, and workflows — legacy systems and partner exports are extended through custom intake, not endless services work.
No. The Intake Layer connects to existing systems and prepares their data for the stack. Teams keep using their current PLM, ALM, ERP, CAD, document, and ticketing tools.
Yes. The architecture targets enterprise and engineering environments, including controlled, private, and on-prem deployments. Read-only ingestion is supported where compliance demands it.
DCH uses the mapped data to build the governed context graph. M4AI and application surfaces then reason over and expose that context — every entity keeps its source lineage, version, and ownership.
Bring your engineering sources into context.
Start with one source. Preserve its lineage and meaning. Build governed context from there.
Connect any engineering source into context.
Bring data from PLM, ALM, ERP, CAD, requirements, FMEA, documents, tickets, repositories, files, databases, and data lakes into one governed context layer.
Intake is not just extraction. Context64.ai preserves source meaning, relationships, lineage, ownership, and engineering semantics — so DCH can build a living context graph.
Connect the systems where engineering work already happens.
40+ source patterns, organised by where they live in your stack. Hover a tile to see the context it produces. The intake layer extends with custom sources on demand.
Most systems move data. Intake preserves meaning.
The hard part isn't extraction. It's keeping engineering meaning intact — relationships, lineage, ownership, version, semantics — so the context layer downstream is governed, not just full.
Before — raw source artifacts
As-isAfter — governed engineering entities
DCH-readyAgents and workers built for intake.
Context64.ai uses intake agents, workers, connectors, and workflows to handle different source types and transformation patterns. Pick an agent on the left to see its pipeline.
- schema: erp_prod.dbo
- tables: 238
- auth: read-only · service acct
- fk → relationshipedge
- columns → attributesattr
- rows → entity instancesent
- Component×412
- Supplier×38
- PartOrder×2,841
From raw source to governed context.
Five stages, every one observable. Read-only by default. Lineage and version-aware all the way to DCH.
Connect
Authenticate, configure access, and define source scope — read-only where required.
auth · scope · scheduleExtract
Read data, metadata, structure, documents, and relationships from the source.
data · metadata · linksMap
Map source objects into engineering entities, relationships, and ontology concepts.
entity · edge · attrGovern
Preserve lineage, access rules, versioning, ownership, and DCH-readiness.
lineage · ACL · versionActivate
Make context available to DCH, M4AI agents, and application surfaces.
retrieve · reason · actBuilt from industrial engineering reality.
Context64.ai was developed with deep exposure to industrial engineering environments. That matters because engineering data is not generic business data.
Requirements, simulations, CAD structures, tests, changes, failure modes, approvals, and supplier data all carry domain meaning. Our intake preserves it. Developed with German OEMs and industrial engineering partners.
- Engineering-aware mapping
- Ontology-driven intake
- Version & lineage preserved
- Relationship extraction
- Read-only ingestion mode
- Governed access
- On-prem / private deployment
- Source integrity preserved
Have a source we don't list? We can build it.
Engineering environments are full of legacy systems, custom databases, internal tools, partner portals, and exports that don't fit standard connector catalogs. The Intake Layer extends through custom agents, workers, and workflows.
Anything that exposes data
Custom agent + mapping rules
Governed context, queryable
Common questions about intake.
If you don't see your question here, send it directly — we route intake-specific questions to the engineering team that owns this layer.
Start with the systems you already have.
Connect your engineering sources once. Turn them into governed context for teams, agents, and applications. No system replacement. No data lake migration.