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Ignition SCADA Private AI Layer Blueprint Generator

This generator turns your environment details into a structured architecture plan for adding a private AI assistant to Ignition SCADA. 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.

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Ignition SCADA Private AI Layer Blueprint Generator

Private AI Layer Architecture Plan: Ignition SCADA

Company: Acme Precision Ltd System: Ignition SCADA (Ignition 8.1) Data layout: PostgreSQL Hosting: On-prem server Sites in scope: Plant 1 (pilot), Plant 2 (phase 3) Named users: 80 Target go-live for phase 1: 2027-02-01

1. Context and scope

Acme Precision Ltd runs Ignition SCADA (Ignition 8.1) 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: Ask about downtime, alarms and tag history in plain language, find tags by description, draft shift and downtime summaries, link downtime reasons to documents and work orders.

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

ComponentDesignNotes for Acme Precision Ltd
Data access pathRead-only database account on the Tag Historian and Alarm Journal database plus approved Named Queries or WebDev endpointsRead-only identity, reviewed by IT and controls owners. Data layout: PostgreSQL.
Retrieval indexTag tree export (paths, descriptions, units, tag groups) plus SOPs, downtime reason guides and maintenance notesRebuilt on a schedule; source permissions stored with each chunk.
Model servingOpen-weight model served with vLLM or Ollama on local GPUsGPU available: 1 x 48 GB class. Sized for 80 named users; see the sizing calculator.
Tool serverMCP or HTTP server exposing a small set of approved read operations with time and row capsNo free-form queries from the model. Every operation is parameterised and reviewed.
Identity and permissionsIgnition roles, security zones and tag permissions mapped to tool-server allow listsNegative tests per role before each phase gate.
AuditAppend-only log of question, user, sources, tool calls, row counts and answerReadable by security. Retention per site policy. Hosting: On-prem server.

3. Security controls mapped to your inputs

ControlHow it is applied
Data residencyModel, 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 siteTags from restricted lines, recipe values, operator identities, any export-controlled program data.
Least privilegeRead-only service identity; per-role allow lists; no admin accounts for the assistant.
Write-backNo PLC tag writes. Downtime or maintenance records only, via validated Named Queries or handlers with human approval.
Air-gap readinessModel weights and containers imported on approved media; embeddings run locally; no runtime internet dependency.
MonitoringAlerts on unusual query volume, denied-access spikes and tool errors.

4. Rollout in three phases

PhaseScopeExit 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 80 usersAnswer quality reviewed on a fixed question set; permission negative tests passed; audit log reviewed by security
2: DraftsDrafted shift summaries, deviation write-ups and reports that a person edits and ownsEdit rate and time saved measured; no unreviewed output enters a controlled record
3: Approved actionsRecords or requests created in downstream systems through supported APIs after human approvalApproval flow tested; rollback and audit proven; extension to further sites considered

5. Open questions for IT

1. Which database engine hosts the historian and alarm journal, and is a read replica available? 2. Are history tables partitioned monthly (default) and what is the retention and pruning policy? 3. Which identity provider or user source does the Gateway use, and can roles be read by the tool server? 4. Will access go through WebDev endpoints, Named Queries called by a service, or direct read-only SQL? 5. Which custom business tables (downtime reasons, production counts) are in scope and who documents them? 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

IT / infrastructure / Date
Controls or OT owner / Date
Information security / Date
Business sponsor / Date

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 Ignition 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 Ignition SCADA.

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.

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