The board wants "an AI strategy"
You've been asked to deliver AI outcomes. Before you commit budget and your credibility, get an honest read on whether the foundation can carry it — and what to fund first.
An MES, historian, or data-lake project
Large data projects fail when they're scoped without a foundation view. Pressure-test the plan before the PO goes out.
Comparing platforms or integrators
A vendor-neutral read on what you actually need — so you buy to a real architecture, not a sales deck.
A pilot that won't scale
The pilot worked in the lab and dies in production. Find the foundational reason, named and sequenced, instead of blaming the model.
"Why isn't predictive maintenance working?"
Models need complete, contextualized, trustworthy signals. We show whether your data can carry the use case — before you scale it.
AI quality inspection across lines
What it takes to run inference reliably, plant-wide — and the gaps that will quietly undermine it.
What you walk away with
A clear verdict, the specific foundations that gate your goal, the order to fix them, and language you can take straight to your board. The free online read is preliminary; the board-ready roadmap is the first paid step that turns it into a funded, sequenced plan.