We design and build production AI for investment teams and factories, on your own data, inside your own environment.
Forty borrower reports a quarter, read by hand. Covenant drift was found late.
A reader that checks each report against the credit agreement and drafts the watchlist memo, with a page reference on every number.
Metallic particles that only showed up as field failures months later.
Vision models on the plant's own microscope captures, tied to lot and station, with an estimate of what each particle does to cycle life.
The machine data said sixteen minutes down. The line had lost twenty-one, because the next cell starved.
A model of the plant that shows the second number. It became VitalFusion Edge.
Before any model is trained, we write down with your team what a covenant, a lot or a line stoppage actually is. The AI reasons over those definitions, which is why its answers can be checked.
One idea, two kinds of client. A portfolio on the left, a plant on the right. Select a node to read what it means.
01 · Frame the decision. Start from the decision someone must make: an allocation, a line stoppage, a sign-off. Work back to the data and model that would change it.
Write to us with a sentence or two about it. We will suggest a 30-minute working session on your actual data, not a pitch.
or write to contact@vitalfusionai.com