Canada + on-prem AI
AI for ERP in Canada: Controlled Goods, PIPEDA, and On-Prem Design
Short answer
Canadian aerospace and defense suppliers running SAP, Oracle, Infor LN, or IFS can add AI to their ERP without moving Controlled Goods Program data or ITAR-controlled technical data outside a network they control. The practical design is a private LLM grounded on ERP, PLM, and quality data, deployed on infrastructure the company or a Canadian data residency provider operates, with PIPEDA and Quebec Law 25 obligations satisfied by architecture rather than by a vendor's terms of service.
- ERP
- SAP S/4HANA, Oracle E-Business Suite, Infor LN, IFS Cloud
- Industries
- Aerospace, Defense, Electronics
- Written for
- CIO
If your plant is registered under Canada's Controlled Goods Program, the first question in any AI conversation is not what the model can do, it is where the data goes. A generic SaaS copilot that routes prompts through a US or EU cloud, or that trains on customer input by default, creates exactly the kind of uncontrolled access CGP screening exists to prevent. That rules out most off-the-shelf enterprise AI products for the controlled-technical-data slice of your ERP, even though the rest of the business, quoting, scheduling, standard reporting, has nothing controlled about it at all.
Layered on top of CGP is a federal and provincial privacy regime that most US-headquartered AI vendors are not built for. PIPEDA sets the federal baseline for how personal information is collected, used, and disclosed, and Quebec's Law 25 goes further for any operation with Quebec employees or customers, including specific obligations around automated decision-making and profiling that a poorly scoped AI agent can trip without anyone intending it to. A Montreal-area supplier answering to both regimes at once needs an AI design that treats consent, purpose limitation, and data minimization as architecture, not as a checkbox in a vendor questionnaire.
Much of Canada's aerospace and defense manufacturing base sits downstream of US primes, which means ITAR-controlled technical data crosses the border into Canadian plants under license and stays subject to US export control even after it lands. Montreal's aerospace cluster, along with defense electronics shops across Ontario and the Prairies, runs on a mix of SAP, Oracle E-Business Suite, Infor LN, and IFS Cloud, and the AI question for these plants is the same one they already answer for every other system: who is allowed to see this data, and can we prove it.
Done well, AI on top of this ERP mix starts narrow and useful on day one: a planner asking, in English or French, which open orders are at risk this week; a quality engineer getting a first-draft NCR from a notification instead of a blank form; an estimator pulling comparable job costs before quoting. None of that requires the model to leave the building, and none of it requires waiting for a multi-year modernization program to finish first.
What usually gets in the way
The problems we hear most from cio teams running SAP S/4HANA.
Controlled technical data has no automatic tag
Drawings, BOM attachments, and engineering change orders linked into the ERP or PLM system are not consistently marked as CGP or ITAR-controlled, so nobody downstream can be certain a document is safe to paste into a chatbot or attach to an email outside the registered facility.
Registered personnel become a bottleneck for routine questions
Only CGP-registered staff can legally view certain drawings and specs, which means simple ERP questions that happen to touch a controlled item get routed to a small group of people instead of being answered in seconds by the person who actually needs the number.
Bilingual shop floors get English-only tools
Quebec plants run day-to-day on the shop floor in French, but most enterprise AI copilots are built, tested, and documented in English first, so adoption lags exactly where the labor shortage is tightest.
Public LLM terms of service conflict with export control screening
Standard API terms for consumer and even many enterprise AI products allow logging, retention, or training on submitted content, which is incompatible with the access controls a CGP-registered organization has to demonstrate during a Public Services and Procurement Canada review.
MRP and PO exception backlog outpaces headcount
Reschedule and expedite messages, supplier confirmation follow-up, and open-order triage grow with order volume, not with the number of planners a plant can afford to hire, and dashboards alone do not close that gap.
Where AI earns its place in SAP S/4HANA
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Controlled-data-aware document classification
Flags likely CGP or ITAR-controlled attachments, drawings, and specs linked to ERP and PLM records before they are copied into a quote package, email, or shared drive.
Touches: Engineering change orders, drawing attachments, PLM-to-ERP BOM links, document control fields
Outcome: cuts the manual review step for outbound quote and RFQ packages from a scheduled compliance check to a real-time flag at the point someone tries to attach the file
Bilingual natural-language query over open orders and inventory
Planners and customer service reps ask questions in English or French and get answers grounded in live ERP data instead of waiting on a report request.
Touches: Sales order headers and lines, inventory balances, MRP action messages
Outcome: answers routine order-status and stock questions in seconds rather than the hours it takes to get a custom report built and rebuilt
MRP exception triage
Groups and prioritizes reschedule, expedite, and cancel messages so planners work the exceptions that actually matter first.
Touches: MRP action messages, purchase order lines, supplier confirmations
Outcome: cuts manual triage of routine reschedule and expedite messages from hours to minutes for the bulk of low-risk lines
AS9100 NCR and CAPA drafting
Drafts a first-pass root cause narrative and containment plan from a quality notification, for the quality engineer to review and finalize.
Touches: Quality notifications, NCR records, 8D worksheets
Outcome: gets a defensible first draft in front of the reviewer in minutes instead of the engineer starting from a blank template every time
Supplier and export screening copilot
Checks a new supplier, ship-to, or customer against export screening and CGP registration status before a purchase order or sales order is released.
Touches: Vendor master, customer master, purchase order and sales order headers, screening status fields
Outcome: catches an unscreened counterparty before the order releases instead of during a post-hoc audit
RFQ-to-quote drafting from historical job costs
Pulls comparable historical job or work order costs and routing data to give estimators a grounded starting point for a new quote.
Touches: Job and work order costing history, routing data, quote header and line records
Outcome: gives estimators a working draft instead of a blank quote, with every figure traceable back to a specific prior job
Bilingual shop floor work instruction assistant
Surfaces the current routing, revision-controlled work instructions, and traveler for the job an operator is running, in the operator's preferred language.
Touches: Routings, work instructions, traveler documents, revision control fields
Outcome: shortens the time a new operator needs to find and follow the correct instruction without pulling a supervisor away from the line
Reference architecture
The architecture keeps controlled and personal data inside a boundary the company already controls, and treats language and access scope as first-class design constraints rather than afterthoughts.
- 1
ERP and PLM connectors
Read-only connectors into SAP (OData, BAPI/RFC), Oracle E-Business Suite (interface tables, concurrent program outputs), Infor LN (BODs, ION), and IFS Cloud (projections), plus a link into the PLM or document control system for drawing metadata.
- 2
Data and semantic layer
A permissioned index that mirrors the ERP's own role and site security, with a classification tag for CGP or ITAR-controlled content so the retrieval layer can exclude it from users who are not registered to see it.
- 3
Model serving
Open-weight models served on customer-owned or Canadian-resident GPU infrastructure, sized to the plant's actual query volume rather than a hyperscale default.
- 4
Retrieval and agents
Bilingual retrieval-augmented generation grounded in the connected ERP and document data, with any write-back action, a status update, a released order, a sent quote, gated behind explicit human approval.
- 5
Governance and audit
A query and action log kept separately from production ERP audit trails, built to answer a CGP compliance review or a PIPEDA/Law 25 access request without a special data pull.
Integration notes for your ERP team
- SAP connections use OData services and BAPI/RFC calls scoped to read-only roles that mirror existing SAP authorization objects.
- Oracle E-Business Suite integration reads from interface tables and standard concurrent program outputs rather than direct table access, to stay upgrade-safe.
- Infor LN integration goes through BODs and ION, keeping the AI layer decoupled from direct database access to session data.
- IFS Cloud integration uses projections, avoiding custom database views that would break on the next platform update.
- Authentication rides on the existing Active Directory or SSO setup, with CGP-cleared group membership scoped directly into the retrieval layer's access control.
- Every deployment starts read-only; any write-back, a status change, a released quote, a completed task, requires an explicit human approval step before it commits.
- Both English and French are supported at the UI and document-retrieval level, with automatic language detection on incoming queries.
Deployment options
Air-gapped on-prem
CGP-registered facilities and ITAR pass-through suppliers
Model serving and retrieval run entirely inside the plant network with no outbound internet path, matching the same physical and personnel security controls already in place for controlled drawings.
Private or sovereign cloud in Canada
companies that want managed infrastructure but need Canadian data residency for PIPEDA and Law 25
The model and data stay inside a Canadian data center region, useful for multi-site groups that want central administration without the capital cost of GPU hardware at every plant.
Hybrid
groups with both CGP-registered and unregistered sites
Controlled-facility deployments stay fully air-gapped while non-controlled divisions use a shared private cloud instance, with the classification tag in the semantic layer deciding which content each deployment can ever see.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
Controlled Goods Program (CGP)
Access to controlled technical data in the AI layer is scoped to registered, security-assessed individuals only, mirroring the same visibility controls already applied to the underlying ERP and PLM records.
PIPEDA
Personal information used in AI features is limited to what a documented business purpose requires, with retention, access, and correction handled through the same processes as the rest of the ERP.
Quebec Law 25
Automated-decision features are disclosed and explainable, and any profiling capability is scoped, logged, and subject to human review rather than left to run unsupervised against Quebec employee or customer data.
ITAR deemed export exposure
For suppliers handling US-origin technical data under license, the AI deployment is designed so no ITAR-controlled content ever transits a foreign national's access path or a non-US, non-Canadian cloud service.
Canadian Export Control List (Global Affairs Canada)
Export screening fields already tracked in the ERP feed the AI layer's guardrails, so an unscreened counterparty or an unclassified export item is flagged before, not after, an order is released.
Where Netray fits
ERPray
Question answering and dashboards across SAP, Oracle, Infor LN, or IFS Cloud data, respecting the same role-based access and CGP classification already in place.
Custom build
A controlled-data-aware document classification agent and export screening copilot are specific enough to the plant's document control setup to warrant a scoped custom build alongside ERPray.
How an engagement runs
Phase 1 . 2-3 weeks
Discovery
- -Data classification review (CGP, ITAR, personal information) across ERP and linked document systems
- -Access model mapped to existing CGP registration and role structure
- -Deployment option recommendation with a data residency and network diagram
Phase 2 . 6-8 weeks
Pilot
- -Working natural-language query pilot for one department (planning, quality, or estimating)
- -Read-only connector to the primary ERP with role-mirrored access controls
- -Bilingual UI validation with actual shop floor and office users
Phase 3 . 8-12 weeks after pilot sign-off
Production
- -Hardened deployment on the agreed on-prem or Canadian private cloud environment
- -Audit logging aligned to CGP and PIPEDA/Law 25 evidence requirements
- -Runbook and internal admin training for ongoing operation
Phase 4 . ongoing
Scale
- -Additional use cases (NCR drafting, supplier screening, quote drafting) added incrementally
- -Rollout to sister sites with shared governance but site-specific access scoping
- -Quarterly review of model performance, access logs, and new controlled-data classifications
Questions to ask any vendor, including us
A short list that separates real SAP S/4HANA AI work from a chatbot demo.
- Where does the model actually run, and can you show me the network diagram, not just a compliance letter?
- Does any prompt, document chunk, or output ever transit a server outside Canada, even for logging or monitoring?
- How is access to CGP-controlled or ITAR-controlled content scoped in the AI layer versus in the underlying ERP?
- What happens to a query and its answer, is it logged, where, and who can read that log?
- Can the system operate with zero outbound internet connectivity if the facility requires it?
- How do you handle French-language content and queries, and has that actually been tested with real users?
- What does an exit look like if we want to bring this in-house or switch vendors in two years?
Frequently asked questions
Can we use a public AI service like a hosted chatbot for CGP-controlled ERP data?
Not for controlled content. Most public and even enterprise-tier AI services route prompts through infrastructure and access paths that CGP registration was designed to prevent, and their terms of service typically allow logging or retention that a CGP compliance review would flag. Non-controlled business data, standard scheduling questions, general reporting, has more flexibility, but the safest design treats the whole ERP integration as controlled by default and carves out exceptions deliberately.
Does PIPEDA or Quebec Law 25 apply if our AI tool only touches inventory and order data?
If any of that data includes names, employee identifiers, or customer contact details tied to orders, both regimes apply to that portion. Law 25 adds specific obligations around automated decision-making, so an AI feature that recommends or triggers an action affecting a person, a credit hold, a supplier flag, needs disclosure and a human review path, even if most of the underlying data is inventory rather than personal information.
How does ITAR fit in if we are a Canadian company, not a US one?
Many Canadian aerospace and defense suppliers receive US-origin technical data under an ITAR license or a Technical Assistance Agreement, and that data stays ITAR-controlled after it crosses the border. An AI deployment touching that data needs the same access discipline as the license itself requires, typically meaning no foreign national access and no routing through non-US, non-Canadian infrastructure, regardless of where your company is headquartered.
What does a realistic first AI use case look like for a CGP-registered plant?
Something narrow, low-risk, and immediately useful: natural-language query over open orders and inventory for planners, or first-draft NCR text from a quality notification. Both stay well clear of controlled technical data while proving out the connector, access model, and bilingual support the rest of the roadmap depends on.
Do we need new hardware to run this on-prem?
It depends on scale. A single-plant deployment answering a few hundred queries a day can run on a modest GPU server, often one that fits in an existing server room rack. Larger, multi-site deployments or heavier document-retrieval workloads need more capacity, but sizing should be based on measured query volume from the pilot, not a vendor's default recommendation.
How long before we see value?
A focused pilot, one department, one ERP, read-only, typically shows usable results in six to eight weeks. Getting to a production deployment with full audit logging and governance in place usually takes another two to three months after the pilot proves the use case is worth hardening.
Can the same deployment serve both our Quebec and Ontario plants?
Yes, with care. The retrieval and access layer needs to respect site-specific and language-specific scoping, and the governance model needs to satisfy Law 25 for Quebec operations even if the rest of the group only needs to meet PIPEDA. A shared private cloud deployment with per-site access boundaries usually works better than either a single undifferentiated instance or fully separate systems per plant.
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Talk it through with an engineer who knows SAP S/4HANA
Bring one real question your team cannot answer from the ERP today. We will map the data path, the model, and where it runs, and tell you honestly if AI is the wrong tool for it.