Almost every procurement organisation is experimenting with AI. Far fewer have moved beyond pilots. The reasons are familiar: spend data scattered across systems, supplier records that disagree with each other, unclear ownership of decisions, and operating models that were never designed for automated workflows.
In industrial procurement there is another reason. The value of a model depends on the category knowledge behind it.
The category problem
An automated quote comparison can line up prices, lead times and payment terms in seconds. What it cannot do without help is recognise that one supplier has quoted a forging route with heavy machining and another a near-net-shape forging with almost none, or that one casting quote excludes the heat treatment the others include. It will compare the wrong things very efficiently.
Start from decisions
We start by asking which decisions would be better with faster or better intelligence. Which parts should be re-sourced? Which quotes deserve a challenge? Which suppliers are becoming a risk? Which commodity movements should trigger a price review? Each decision defines the intelligence it needs: spend, supplier, market, cost-driver or risk.
Build the intelligence a decision needs. Then automate the work around it.
Then automate the repeatable
Once the intelligence is sound, automation earns its place: RFQ and quote-comparison workflows, cost-model automation, opportunity engines, supplier dashboards and alerts, knowledge copilots and negotiation preparation. Our specialists stay in the loop, defining what should be compared and reviewing what the system concludes.
We do not sell a platform. We build on the data and systems a client already has, and we measure automation the way we measure any other lever: by faster cycles, better decisions or captured savings.