AVEVA System Platform and Historian Private AI Layer Blueprint Generator
This generator turns your environment details into a structured architecture plan for adding a private AI assistant to AVEVA System Platform and Historian. It covers components, security controls, rollout phases and the questions your IT and controls teams need to answer, and it exports to PDF or Word.
Fill in
AVEVA System Platform and Historian Private AI Layer Blueprint Generator
Private AI Layer Architecture Plan: AVEVA System Platform and Historian
Company: Acme Precision Ltd System: AVEVA System Platform and Historian (System Platform 2017 / 2017 U3) Data layout: Historian on SQL Server (dedicated server) Hosting: On-prem server Sites in scope: Plant 1 (pilot), Plant 2 (phase 3) Named users: 150 Target go-live for phase 1: 2027-02-01
1. Context and scope
Acme Precision Ltd runs AVEVA System Platform and Historian (System Platform 2017 / 2017 U3) and wants an AI assistant that works on the data and documents already held on site, without moving that data to a third-party cloud. Hosting model: On-prem server.
Main use cases: Query tag history and alarm counts in plain language, locate the right tag by area and description, explain batch deviations using documents and trends, draft shift summaries.
Out of scope for the assistant: control writes of any kind, changes to controlled records without a person committing them, and any access beyond the roles each user already holds.
2. Components
| Component | Design | Notes for Acme Precision Ltd |
|---|---|---|
| Data access path | Read-only SQL login on the Historian Runtime database views via named, parameterised tools with time and row caps | Read-only identity, reviewed by IT and controls owners. Data layout: Historian on SQL Server (dedicated server). |
| Retrieval index | Tag catalog (name, description, unit, Galaxy area) plus SOPs, batch records and alarm philosophy documents | Rebuilt on a schedule; source permissions stored with each chunk. |
| Model serving | Open-weight model served with vLLM or Ollama on local GPUs | GPU available: 1 x 48 GB class. Sized for 150 named users; see the sizing calculator. |
| Tool server | MCP or HTTP server exposing a small set of approved read operations with time and row caps | No free-form queries from the model. Every operation is parameterised and reviewed. |
| Identity and permissions | Galaxy areas and roles translated into per-role tag allow lists enforced in the tool server | Negative tests per role before each phase gate. |
| Audit | Append-only log of question, user, sources, tool calls, row counts and answer | Readable by security. Retention per site policy. Hosting: On-prem server. |
3. Security controls mapped to your inputs
| Control | How it is applied |
|---|---|
| Data residency | Model, embeddings, index, tool server and logs all run on Acme Precision Ltd infrastructure (On-prem server). No outbound calls for inference. |
| Data that must never leave the site | Recipe parameters, tags from restricted areas, alarm text that reveals protected process details, any export-controlled program data. |
| Least privilege | Read-only service identity; per-role allow lists; no admin accounts for the assistant. |
| Write-back | No writes to runtime attributes. Drafts only; approved records go to CMMS or ERP through their APIs. |
| Air-gap readiness | Model weights and containers imported on approved media; embeddings run locally; no runtime internet dependency. |
| Monitoring | Alerts on unusual query volume, denied-access spikes and tool errors. |
4. Rollout in three phases
| Phase | Scope | Exit criteria |
|---|---|---|
| 1: Read-only questions (target 2027-02-01) | One area or line, read-only questions with sources shown, for a pilot group of the 150 users | Answer quality reviewed on a fixed question set; permission negative tests passed; audit log reviewed by security |
| 2: Drafts | Drafted shift summaries, deviation write-ups and reports that a person edits and owns | Edit rate and time saved measured; no unreviewed output enters a controlled record |
| 3: Approved actions | Records or requests created in downstream systems through supported APIs after human approval | Approval flow tested; rollback and audit proven; extension to further sites considered |
5. Open questions for IT
1. Is there a Historian reporting tier or replica, or would analytical queries land on the collecting server? 2. Which SQL Server edition and version hosts the Runtime database, and who is the DBA owner? 3. How are Galaxy areas and security roles defined, and can they be exported to build allow lists? 4. Is alarm logging to SQL enabled, and for how long is it retained? 5. Which tags use cryptic names, and who can supply descriptions for the in-scope set? 6. Who owns the model and index lifecycle (updates, re-indexing, evaluation) after go-live? 7. Which approval board signs off each phase gate?
Approvals
This is a planning starting point, not a design guarantee. Confirm interfaces, versions and licensing against the vendor documentation for your installed release before committing to a build. Keep the assistant read-only against controlled records.
Architecture review for your System Platform AI layer
Send your generated plan and a Netray engineer will review it with you and flag gaps in access, permissions and rollout. Optional.
Optional. The tool and every export work without this. No spam.
How to use the generator
Fill in the company, version, data layout, hosting model, named users and sites. Write the main use cases in plain language and, just as important, list the data that must never leave the site. Choose the GPU capacity you have or expect. The output document updates as you type. Export it, circulate it to IT, controls and security, and use the open questions as the agenda for a first review meeting.
- Start with one area and one pilot group
- Write the never-leave list first
- Be honest about GPU availability
- Treat the output as a draft for review, not a final design
What the plan contains
The plan has six parts: scope, a components table, security controls tied to your inputs, a three-phase rollout, open questions and an approvals block. The components table covers the six things every private AI layer needs: a data access path, a retrieval index, model serving, a tool server, identity and permissions, and audit. Each is stated in terms specific to AVEVA System Platform and Historian.
Design principles behind it
The design follows a few principles. The assistant reads through approved, parameterised tools instead of free-form queries. It inherits the permissions each user already has. It never writes to control systems and writes to business systems only through supported APIs after a person approves the payload. Everything it does is logged. Inference runs on your own hardware, so the plan works in an air-gapped site as well as an on-prem one.
Common mistakes to avoid
The usual failures are scoping too widely, giving the assistant an admin account for convenience, skipping negative permission tests, returning raw time-series or record dumps to the model instead of summaries, and ignoring data quality such as missing tag descriptions. Fix those in phase 1 and the later phases go much more smoothly.
Frequently Asked Questions
Does this work for an air-gapped site?
Yes. Choose Air-gapped as the hosting option. The design uses local models, a local index and a local audit log, with software imported on approved media and no runtime internet dependency.
Is the generated document a final design?
No. It is a structured starting point for review. Confirm interfaces, versions and licensing with the vendor documentation for your release, and adjust the components to what your IT team approves.
Which model should we use?
The plan assumes an open-weight model served with vLLM or Ollama. Which one fits depends on GPU memory, language needs and evaluation on your own question set, so test two or three candidates in phase 1.
Can the assistant change data in the system?
Not directly. The plan restricts it to read access, with write-back limited to supported APIs in business systems after a person approves each payload. Control-system writes are out of scope.
If you want a second pair of eyes on this plan for your environment, Netray designs and builds private AI layers for on-prem industrial and ERP systems, and can review the document with your team.
Related Tools
AVEVA System Platform and Historian AI Assistant Sizing and Value Calculator
Estimate peak concurrency, GPU count, index size, hours saved and payback for a private on-prem AI assistant on AVEVA System Platform and Historian.
MES & Shop FloorAVEVA PI System Private AI Layer Blueprint Generator
Fill in your environment and get an architecture plan for a private AI assistant on AVEVA PI System: components, security controls, rollout phases and open questions for IT.
MES & Shop FloorRockwell FactoryTalk Private AI Layer Blueprint Generator
Fill in your environment and get an architecture plan for a private AI assistant on Rockwell FactoryTalk: components, security controls, rollout phases and open questions for IT.
More MES & Shop Floor tools
See all- Oracle Fusion OEE Work Center Calculator
- Infor VISUAL Shop Labor Ticket Log Worksheet
- VISUAL Shop Load Versus Capacity Calculator
- VISUAL Shop Resource Capacity and Load Calculator
- IFS Shop Order Status and Lifecycle Reference
- Plex Smart Manufacturing Data Source Request Template
- Plex Production Scheduling Checklist
- Plex Downtime Reason Log and OEE Worksheet
- Plex Go-Live Readiness Sign-Off Template
Keep exploring
Free tools
- AVEVA System Platform and Historian AI Assistant Sizing and Value CalculatorEstimate peak concurrency, GPU count, index size, hours saved and payback for a private on-prem AI assistant on AVEVA System Platform and Historian.
- AVEVA PI System Private AI Layer Blueprint GeneratorFill in your environment and get an architecture plan for a private AI assistant on AVEVA PI System: components, security controls, rollout phases and open questions for IT.
- AVEVA PI System AI Assistant Sizing and Value CalculatorEstimate peak concurrency, GPU count, index size, hours saved and payback for a private on-prem AI assistant on AVEVA PI System.
ERP fixes and how-tos
- AI on AVEVA System Platform and Wonderware Historian, Fully On-PremArchitecture for a private AI assistant on AVEVA System Platform and Wonderware Historian: SQL views, Galaxy areas, read-only access, local GPUs, audit log.
- How to Add AI to the AVEVA PI System On-Prem, Without Moving Data to a CloudHow to add a private AI assistant to the AVEVA PI System on-prem: PI Web API, PI AF, read-only access, tag security mirroring, local GPUs and an audit log.