AdvancedSiemens Teamcenter (on-prem)Private AI layer / SOA / Access Manager

How to add AI to Teamcenter on-prem without moving your PLM data

Question

how to add AI to Teamcenter on-prem

Also searched as

Teamcenter AI integration without cloud
Teamcenter private LLM architecture
chat with Teamcenter data on-premises
Teamcenter air-gapped AI assistant

Short answer

Run an open-weight model on your own GPUs, expose a small set of read-only Teamcenter actions through a tool server that calls the Teamcenter SOA web services as the asking user, and index documents and datasets locally. Teamcenter's Access Manager keeps deciding who sees what, so the assistant cannot show a user anything their own session could not open.

Applies to: Teamcenter 13.x, 14.x and 2312 or later, Oracle or SQL Server database, Active Workspace optional. Details vary by version and BMIDE customisation.

Reference build for a private AI layer on Teamcenter

1Inventory what the assistant must answer: item and revision lookups, BOM questions, change status, dataset and document content, workflow tasks. Each question class maps to a Teamcenter data path.
2Pick the access path per class. Use the Teamcenter SOA services (Core, Query, Cad and Workflow service families) for anything permission sensitive. Use a database replica only for aggregate analytics on attributes that carry no restrictions.
3Stand up a tool server (MCP or plain HTTP) with a handful of read actions such as find_item, get_revision_properties, expand_bom, list_open_changes, search_documents. No free-form SQL against the production database.
4Build a retrieval index over dataset text (PDF, Office, text exported from the FMS volumes) and over item metadata. Store the ACL context of each chunk alongside the vector so retrieval can be filtered.
5Serve an open-weight model with vLLM (or Ollama for a pilot) on local GPUs, behind your own reverse proxy, with no outbound route. Keep the embedding model local too.
6Pass the user identity from your SSO into every tool call, so SOA calls run with that user's Teamcenter group, role and project. Log prompt, tools called, object UIDs returned and answer to an append-only audit store.
7Roll out in phases: read-only questions for one group, then drafted outputs (change summaries, review packages), then approved actions through supported SOA operations with a human click.

Real data access paths in Teamcenter

Teamcenter stores its object model in Oracle or SQL Server through the POM (persistent object manager). Item, ItemRevision, BOMView and Dataset objects sit in tables such as PITEM, PITEMREVISION and PWORKSPACEOBJECT, with files kept outside the database on FMS volumes. The schema follows your BMIDE data model, so customised properties live in extra tables and the layout differs between sites.

The supported way in is the SOA layer: web services hosted in the Teamcenter web tier, exposed over a JSON REST binding as well as the Java and .NET client bindings. Operations such as saved queries, getProperties and BOM expansion enforce Access Manager rules because they run inside the user's session. Active Workspace adds a Solr based index that can serve fast search.

A direct read from a replica is useful for counting and trending, but it bypasses Access Manager, ip_classification and gov_classification style checks and project scoping. Treat it as analytics only unless you rebuild those rules yourself, which is rarely worth it.

-- Read-only analytics on a reporting replica (verify names against your BMIDE model)
SELECT i.pitem_id, w.pobject_name, w.pobject_desc
FROM PITEM i
JOIN PWORKSPACEOBJECT w ON w.puid = i.puid
WHERE i.pitem_id LIKE 'A-1%'
FETCH FIRST 50 ROWS ONLY;

# Permission-aware read: SOA call through the web tier as the asking user
# Pattern: POST <web tier>/tc/JsonRestServices/<Service-Version>/<Operation>
# Core-2006-03-DataManagement / getProperties with the object UIDs and property names,
# sent with the user's own authenticated session (SSO or login service).

Reference architecture

Keep the layers separate. A Teamcenter web tier and Active Workspace stay as they are. Beside them sit a reporting replica (or a periodic extract), a document extraction worker that reads FMS volumes through a service account limited to released data, a vector index, a tool server and the model host. The model host holds the open-weight model on vLLM and nothing in this chain needs the internet.

For an air-gapped site, import model weights and Python wheels through your normal media transfer process, pin versions, and keep the container registry local. Retrieval, embeddings, reranking, the tool server and the audit store all run on the same enclave. The AI layer then has the same network posture as the PLM itself.

Change data capture is optional. Teamcenter does not give you a single clean change feed, so most sites re-index by last modified date per object class on a schedule, plus event hooks for released revisions if the team already uses workflow handlers for integrations.

Permissions, write-back and audit

Access Manager evaluates a rule tree plus ACLs per object, then project membership and group or role. The assistant should never hold a superuser session. Resolve the caller's Teamcenter identity from SSO, call SOA as that user, and filter retrieval chunks by a stored access marker that is revalidated through SOA before text is shown. Excluded objects appear as 'not found', not as 'restricted', so existence is not leaked.

Write-back stays narrow. Creating a change request draft, attaching a note or starting a workflow are all supported SOA operations. Present the exact payload to the user, require approval, execute under their identity, and log it. Do not let the model emit arbitrary SOA calls or update properties in bulk.

The audit record should hold the user, timestamp, question, retrieved object UIDs and revision IDs, tool calls and answer. For controlled technical data, this record, together with the enclave boundary, is what your export control officer will want to see.

Sizing and phased rollout

A pilot for 20 to 50 engineers with a 7B to 14B class model for routing and a 30B to 70B class model for answers typically fits one to two data-centre GPUs with 48 to 80 GB each. Departmental use for a few hundred named users often lands at two to four GPUs plus a separate smaller box for embeddings and the index. A vector index over a few hundred thousand documents is usually tens of GB, not hundreds.

Phase one is read-only: where used, BOM comparison, revision history, document Q and A. Phase two drafts artefacts: ECR summaries, review checklists, supplier change notices. Phase three triggers supported actions after approval. Measure answer accuracy against a test set of questions your engineers already ask before each phase gate.

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1. Context

Acme Aerospace Systems runs Teamcenter 14.x on Oracle, hosted as: On-prem server. Scope: 400 named users across 3 site(s). Target go-live: 2027-03-31.

Use cases in scope: Where-used and BOM questions, change status, document Q and A over released drawings.

Data that must never leave the site: ITAR controlled technical data, customer proprietary designs, supplier pricing.

2. Components

Component	Design for Teamcenter 14.x	Notes
Data access path	Teamcenter SOA web services as the asking user; reporting replica of Oracle for aggregate analytics only	Replica reads bypass Access Manager, so keep them to unrestricted attributes
Retrieval index	Local vector and keyword index over dataset text and item metadata, with access marker per chunk	Re-authorise through SOA before display
Model serving	Open-weight model on vLLM, GPU: 2 x 80 GB	Local embeddings, no outbound route
Tool server	Named read actions (find item, properties, BOM expand, open changes)	No free-form SQL or free-form SOA payloads
Identity and permissions	SSO identity passed to every call; Teamcenter group, role and project applied by the server	No privileged service session for answers
Audit	Append-only log of user, prompt, tool calls, object UIDs and answer	Retention set by your records policy

3. Security controls mapped to your inputs

Hosting: On-prem server. The model host has no internet route; weights and packages enter through the approved media process.

For the data that must stay on site (ITAR controlled technical data, customer proprietary designs, supplier pricing.): access markers on every retrieval chunk, live permission re-check, and exclusion of any folder or class whose rules cannot be mirrored.

Write-back: none in phase 1 and 2. Phase 3 actions use supported SOA operations with an explicit user approval step.

4. Rollout

Phase	Scope	Exit criteria
1: Read-only	Item, BOM, change and document Q and A for one pilot group	Accuracy on a fixed question set agreed with engineers
2: Drafts	ECR summaries, review packages, supplier change notices	Reviewers accept drafts with limited edits
3: Approved actions	Supported SOA actions after human approval	Audit review passed, rollback tested

5. Open questions for IT

1. Which SSO provider issues identities Teamcenter trusts? 2. Which BMIDE classes and properties carry export control or IP classification data? 3. Where do FMS volumes live and what read path is allowed for extraction? 4. Can a reporting replica of Oracle be provisioned? 5. Who approves model and package imports into the enclave?

Prepared by / Date

PLM owner / Date

IT security / Date

A starting plan, not a design sign-off. Confirm service names, versions and your Access Manager rules with your Teamcenter administrators before build.

Architecture review of your plan

Optional: send the generated plan and get a short written review of gaps in permissions and data paths.

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Common pitfalls

!Reading production tables directly and treating the result as permission safe. Access Manager and project scoping are applied by Teamcenter, not by the database.
!Indexing every dataset into one shared vector store with no access marker, so a retrieval hit can surface text a user could not open.
!Giving the assistant a privileged service account for convenience. Run calls as the user, or filter strictly.
!Assuming schema names are identical across sites. BMIDE customisation and version changes alter tables and property names.
!Letting the model compose free-form SQL or SOA payloads. Expose named, parameterised actions only.
!Ignoring CAD and binary content. Text from JT, CATIA or NX files is not retrievable by a generic text pipeline, so scope expectations.

How an ERP-grounded AI assistant handles this

A grounded assistant on Teamcenter answers 'which open changes affect revision C of this assembly' by calling the saved query and where-used operations as the user, then explains the result and shows which calls it made. Netray builds this as a custom private AI layer, with ERPray style read-only behaviour, local models and an audit trail, delivered with PLM partners where needed.

See ERPray

Frequently asked questions

Can Teamcenter AI run fully air-gapped?

Yes. The model, embeddings, index and tool server all run on local hardware, and Teamcenter does not need internet access. Weights and software arrive through your controlled media transfer process. Updates are then a planned import, not an automatic download.

Is Siemens' own AI enough?

If your data and policies allow the vendor's cloud features and your questions stay inside what they cover, use them first. A private layer makes sense when data cannot leave your network, when you need ERP, MES and PLM questions answered together, or when you want to choose the model.

Does the assistant bypass Access Manager?

It should not. Permission sensitive reads go through SOA as the asking user, and retrieval chunks carry access markers that are rechecked before display. Direct database access is reserved for aggregate analytics on unrestricted attributes.

How is write-back handled?

Only through supported SOA operations, shown to the user as an exact payload and executed under their identity after approval. Nothing is written autonomously, and each action is logged with the object UIDs it touched.

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AI for ERP

Stuck on Siemens Teamcenter (on-prem)?

Talk to engineers who work inside Siemens Teamcenter (on-prem) every week, and who build private AI that answers these questions from your own ERP data.

How to add AI to Teamcenter on-prem without moving your PLM data

Reference build for a private AI layer on Teamcenter

Real data access paths in Teamcenter

Reference architecture

Permissions, write-back and audit

Sizing and phased rollout

Fill in

Teamcenter Private AI Layer Blueprint Generator

1. Context

2. Components

3. Security controls mapped to your inputs

4. Rollout

5. Open questions for IT

Architecture review of your plan

Common pitfalls

How an ERP-grounded AI assistant handles this

Frequently asked questions

Related

How to add AI to Windchill on-prem with Windchill REST Services

How to add AI to ENOVIA and on-prem 3DEXPERIENCE

Teamcenter to SAP BOM transfer: why releases fail and how to fix them

How to add a private AI layer to Infor LX / BPCS on-prem

How to add a private AI layer to Infor System21 on-prem

How to add a private AI layer to Infor EAM on-prem

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