Not a chatbot bolted on the side. Predictive insight and faster decisions from the data you already collect — with a human accountable for anything that touches equipment.
AI + Ignition means turning the data your plant already produces into answers you can act on — predicting failures, finding hidden capacity, and cutting scrap. The AI speeds up how the system gets built and how insight surfaces; the control logic that runs your equipment stays standard, deterministic, and engineer-owned.
Every gateway is already collecting more than any person can read. The value isn't more dashboards — it's the questions those dashboards can't answer on their own:
The nervousness around "AI in industrial" is usually about one specific thing: an autonomous model writing to live tags that move real equipment, with no one accountable. That is not how we work, and it's worth being precise about the difference.
We apply AI in two places — the build loop (accelerating how the Ignition application is developed) and the insight loop (surfacing patterns in your data). In both, a senior engineer designs, reviews, tests, and commissions the result. The system that ships is the same deterministic Ignition logic it has always been.
The same principle applies to work your engineers produce with AI assistance. An Ignition System Health Check samples AI-generated and AI-assisted work alongside everything else and reports on architectural fit and consistency — not style. If what you need is senior judgment in the room rather than a report, that is engineering alongside your team.
No. We use AI to accelerate development and to surface insight from your data. Anything that writes to live equipment is gated, reviewed, and commissioned by a senior engineer. Nothing runs autonomously against live production equipment.
Access is read-first by default, writes are explicitly gated, and integrations are scoped to only what a task needs. Your data stays in your systems, and the deployed logic is standard, deterministic Ignition.
Concretely: which asset is trending toward failure, where latent capacity is hiding, which process conditions drive scrap, and which questions about your operation you can now answer in seconds instead of days.
Often nothing, for routine screens and scripting — that work has genuinely got faster and we would not pretend otherwise. Where we are still worth calling is the part AI does not reliably hold on its own: architecture that stays coherent as a system grows, integration across SCADA, MES and ERP, behavior under load and failure, and a senior engineer accountable for what reaches production.
Talk to an engineer about one question your current dashboards can't answer. Twenty minutes.
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