Start at the point of contact.
A DCS tag, a historian record, and a maintenance document may describe the same piece of equipment in different ways. An AI workflow has to understand that relationship before its answer is useful.
This makes integration part of the reasoning problem. The interface provides access. The equipment model, source quality, and operating context determine what that access means.
An answer needs a place in the work.
It helps to start with a specific decision. What is the team trying to understand? Which sources support that question? Who can tell whether the result is useful?
These questions shape the scope of the integration. A configuration workflow needs a target schema and engineering intent. An alarm investigation needs event timing, related process context, and a procedure the reviewer recognizes. The same connector does not answer both problems by itself.
Make the first proof inspectable.
Choose one asset or task. Agree the source material, the expected output, and the person who will review it. Then test whether the workflow can preserve the meaning of the source as it moves toward an answer.
The first proof should reveal mistakes as well as useful behavior. A wrong equipment mapping, an unclear unit, or a missing timestamp is information about what the system needs next. It should not disappear behind a fluent answer.
Build a stronger foundation for the next workflow.
Puffstack’s approach is to build AI workflows around existing industrial systems. The broader goal is reusable orchestration, grounded in the particular equipment, software, and operating work at hand.
A strategy conversation can begin before a use case is fully specified. Bring the system and the integration problem. That is often where the useful work starts.