Why this exists
Most AI work in manufacturing stalls before it reaches production, and the reason is rarely the model. It is that the data is stale, incomplete or unreachable, and that the pilot was aimed at a problem nobody urgently needed solved. Fixing the data first is what turns AI from a project into a tool people use without thinking about it.
The work
What we do
- Start from a decision that is currently made slowly, badly, or on instinct
- Check the data genuinely supports that decision before building anything
- Put the output where the decision is made, not in a dashboard nobody opens
- Keep a person in the loop wherever being wrong is expensive
Deliverables
What you get
- A decision that measurably improves, rather than a model that scores well
- Honest limits - where it works, and where it should not be trusted
- Something that keeps working as the underlying data changes
Edges
Where it stops
What we don't touch
Decisions that should stay human. We are explicit about which those are rather than quietly automating them.
Done when
a decision that used to rely on guesswork is made better, repeatedly, by people who trust it.
Next step
Start with discovery.
Whether this is the right piece of work is exactly what discovery answers.