I have spent more than fifteen years in industrial reliability. On site with the equipment, and in the engineering office behind it.
Most of those weeks did not go into engineering. They went into exports, documents, and reconciling the same asset names for the fourth time. Every reliability team I have worked with loses time the same way.
That frustration is where Reliability Factory started.
Then AI changed the arithmetic.
A study that used to take weeks can be drafted in days, with more consistency and with time left over to verify it properly. A gap is opening between the teams who put that to work and the teams who do not. I would rather my clients sit on the right side of it.
So I handed the heavy lifting to AI: retrieve, structure, connect. I kept the part that actually needs an engineer, which is deciding what it means.
The knowledge graph came last.
It was not the plan. It is what was left once the digging got fast. With the grind gone, the real problem finally showed up: the context died with the study. Every new case started from zero again.
So I built a working environment that keeps it. Assets, failure modes, events, evidence and decisions, connected and traceable. I call it Factory Brain. It is how I deliver the work, not something you have to buy or run.
What you actually get.
An engineer's conclusion you can challenge, with the evidence attached. Not a score from a black box.
I work one costly problem at a time: an RCA, a Bad Actor Review, a maintenance strategy. Your data never trains a public model, and the knowledge stays yours.