Ignition + ERP + private AI
AI for Ignition SCADA: Ask Tag History, Alarms and OEE Questions, Joined to the ERP
Short answer
AI for Ignition connects a private language model to the Ignition tag historian, alarm journal and any SQL databases the gateway writes to, so engineers can ask about trends, alarms, downtime and production counts in plain English. Joined to ERP orders through its APIs, it runs on-prem beside the gateway and stays read-only.
- ERP
- Inductive Automation Ignition
- Industries
- Manufacturing, Food and Beverage, Energy, Aerospace
- Written for
- Controls Engineer
Ignition by Inductive Automation is a SCADA and industrial application platform used for HMI, historian, alarming, reporting and, with modules such as those from Sepasoft, MES functions. Its architecture is relatively open: a gateway talks to PLCs through OPC UA and drivers, writes history to a SQL database, and exposes screens through Vision and Perspective.
That openness is why an Ignition site often holds years of tag history in standard SQL tables. The tag historian partitions data into tables by time period and keeps tag paths in a separate table, and alarm journal and audit data sit in SQL too. This makes the data accessible, but not friendly: a question about a single line's temperature before a batch failure involves tag path lookups, time ranges and unit conversions.
Most Ignition users build dashboards, and Ignition has good built-in trending and reporting. An AI layer is for the unplanned question, the cross-system question such as which production orders were running during an alarm storm, and for summarising alarm and downtime history for people who do not know the tag naming convention.
This page describes how a private AI layer attaches to Ignition's data, the care required around control systems, and where the native tools or a simpler approach are enough.
What usually gets in the way
The problems we hear most from controls engineer teams running Inductive Automation Ignition.
Tag names are a language of their own
Years of project work leave tag paths with inconsistent folders and naming. Only the person who built the project knows which tag holds good count versus total count, so every investigation starts with asking them.
Alarm floods bury the signal
Alarm journals hold thousands of events per shift. Operators and engineers rarely have time to find which alarms preceded a failure or which are chattering and need rationalisation.
Process data is not tied to production orders
The historian knows time and tag values; the ERP knows work orders and lots. Matching a quality deviation to the process conditions during that order requires manual joining across systems.
Multiple gateways, multiple truths
Plants with several gateways and sites accumulate different tag structures and historians, so enterprise questions need a common model that does not exist.
Control systems need cautious AI use
Anything that could write to a PLC tag is a safety and regulatory concern; teams reasonably refuse AI that is not strictly read-only and auditable.
Where AI earns its place in Inductive Automation Ignition
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Natural-language tag history queries
Ask for the trend of a named process variable across a time range or batch and get a chart and summary with the underlying query shown.
Touches: Tag historian tables and tag path table; ERP production order times for context
Outcome: Engineers spend less time hunting tag paths; the answer is only as good as the tag metadata, so a curated tag dictionary is part of the work.
Alarm journal analysis
Rank alarms by frequency, identify chattering and stale alarms, and list what fired in the minutes before a trip.
Touches: Alarm journal and alarm status data, shift and area attributes
Outcome: Faster alarm rationalisation work and quicker post-incident reviews, supported by evidence rather than recollection.
OEE and downtime Q&A with MES modules
Where an MES module records line states and downtime, answer questions about OEE components by line, product and shift.
Touches: MES module line state, downtime and count tables; ERP products and work orders
Outcome: Production managers get answers without a report build, using the same OEE definitions the plant already agreed.
Process conditions per production order
Retrieve key process parameters during a specific work order or batch window, joined to the order and lot in the ERP.
Touches: Historian values by time window, ERP work orders, lots, routings
Outcome: Quality investigations get process context in minutes; deviations are reviewed by process engineers.
Tag and project documentation assistant
Answer how a tag, UDT or script is used, from project exports and naming standards, with citations.
Touches: Project exports, UDT definitions, script libraries, naming standards
Outcome: New engineers become productive faster and key-person risk falls.
Energy and utility consumption review
Compare energy tags with production volumes from the ERP to find consumption per unit by line and shift.
Touches: Energy meter tags, ERP production quantities and calendars
Outcome: Highlights lines whose energy per unit has drifted; root cause remains an engineering judgement.
Shift report drafting
Draft a shift summary of alarms, downtime and production against plan for a supervisor to edit.
Touches: Alarm journal, downtime data, ERP schedule
Outcome: Supervisors review rather than write the first draft.
Reference architecture
A read-only AI service connects to the SQL databases Ignition writes to, with a curated tag dictionary and views, and calls the ERP through its APIs. A local model runs on separate infrastructure and has no path to the control network or to any tag write.
- 1
Data access
Read-only SQL access to a replica of the historian, alarm journal and any MES or production tables, placed outside the control network.
- 2
Tag dictionary
A curated map from cryptic tag paths to business names, units, equipment and areas, built with controls engineers and maintained alongside the project.
- 3
ERP join
Production order, lot and product context from the ERP via its APIs, aligned to historian time windows.
- 4
Model serving
An open-weight model on your own GPUs, using tool calls to generate and run queries against defined views and returning chart-ready results.
- 5
Controls
No write access to tags or PLCs, network segmentation, query logging, and role scoping by site and area.
Integration notes for your ERP team
- Query the historian SQL tables through views built for the purpose; tag paths live in a separate table and partitions are by period, so do not let the model guess table names.
- Build the tag dictionary first; it is the single biggest factor in answer quality.
- Run the AI against a replica so heavy queries cannot slow the gateway's historian writes.
- Use Ignition's documented interfaces such as the database connections it already writes to; avoid any path that exposes gateway write functions.
- If you use MQTT with Sparkplug B for plant data, topic and metric names can seed the dictionary, but check how they map to historian paths.
- Align time zones and daylight saving handling between the historian, the MES and the ERP before joining by time window.
- Join to ERP orders on line and time window, using order start and end events from the MES or shop-floor integration.
Deployment options
IT-zone on-prem
Most plants
GPU server in the plant IT or DMZ layer, reading a replicated historian database, with no route to the control network.
Central private cloud
Multi-site operators
Replicated historian and alarm data from each gateway into a central store with site-level access.
Air-gapped
Defense or critical infrastructure
Fully isolated environment with data moved under your change control.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
IEC 62443
The AI sits in a higher zone, reads replicated data through defined conduits, and has no path to control zones, which supports your zone and conduit design.
NIST SP 800-82
Read-only, segmented access follows ICS security guidance; the customer owns the risk assessment.
ITAR / EAR and CMMC
Where process data for controlled programs is in scope, hosting inside the customer enclave lets their program set access.
FDA 21 CFR Part 11 and GxP
For regulated process data the AI is a retrieval aid only; electronic records and signatures stay in validated systems.
Where Netray fits
Custom build
The work is a tag dictionary, curated views and a retrieval layer specific to your Ignition projects, joined to your ERP.
DataRay
If the historian, alarm journal and other plant databases need to be explored together on-prem, DataRay fits the chat-with-data pattern.
ERPray
ERPray can supply the order, lot and cost side of the join for supported ERPs.
How an engagement runs
Phase 1 . 1 to 2 weeks
Discovery
- -Network zone and data flow review
- -Historian and alarm data inventory
- -Top questions from engineering and operations
Phase 2 . 3 to 5 weeks
Tag dictionary and views
- -Curated tag dictionary for pilot area
- -SQL views for trends and alarms
- -Read-only replica access
Phase 3 . 3 to 5 weeks
Pilot
- -Q&A for one line
- -Verified answer test set
- -Security review sign-off
Phase 4 . 4 to 8 weeks
Rollout
- -Extension to more areas and sites
- -Dictionary maintenance process
- -Runbook and monitoring
Questions to ask any vendor, including us
A short list that separates real Inductive Automation Ignition AI work from a chatbot demo.
- Where in the network zones will the AI run, and what conduits does it use?
- Can you show that there is no write path to tags or PLCs?
- Who builds and maintains the tag dictionary?
- Is the historian replicated, and what is the query load on it?
- How are time zones and clock drift handled across systems?
- What does Ignition already provide that we should not duplicate?
- How are answers verified before engineers rely on them?
Frequently asked questions
Can AI query the Ignition tag historian?
Yes, since the historian stores data in SQL tables. The reliable approach is a curated tag dictionary and views, with the model calling defined queries rather than inventing table names. Answers show the query and time range so an engineer can verify them.
Is it safe to use AI near a SCADA system?
It can be, if the AI is placed outside the control network, reads a replicated database, and has no write path to tags or PLCs. Your security team should review the design against IEC 62443 zones and conduits. We recommend read-only operation for all initial deployments.
Does Ignition have its own AI features?
Inductive Automation and its module partners continue to add analytics and tooling, so check what is current for your version. Native trending, alarming and reporting remain the right tools for live operations. A separate layer suits cross-system questions and plain-English access for non-specialists.
What about Ignition MES modules like Sepasoft?
If such modules write OEE, downtime and track-and-trace data to SQL, the AI layer can read those tables once they are mapped into business terms. We would confirm the module and version in discovery, as table structures differ between products.
How does it connect to the ERP?
Through the ERP's own APIs or reporting interfaces for production orders, lots and products, matched to historian time windows by line. The join depends on reliable order start and end events, so we check how those are captured early.
Can it predict failures?
A language model is not a predictive maintenance engine. It can summarise history and surface patterns for engineers. Dedicated statistical or machine learning models can be built on the same data if there is a clear failure signal and enough labelled history.
How long does the tag dictionary take?
For one production area, a few weeks with a controls engineer who knows the project. Cleaner naming conventions and UDT usage shorten this. It is a one-time investment per area plus light maintenance when projects change.
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Talk it through with an engineer who knows Inductive Automation Ignition
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.