Trained to validate findings, not just present them — to the standard of organisations where a 5% error is not acceptable. That discipline applies to every automotive and manufacturing data or AI engagement, whichever vertical it sits in.
Most engagements are data problems, not AI problems. We will say so in week one — not after the invoice. The right answer is sometimes a dashboard or a clean query, not a model. Occasionally it is: don't spend money on this.
Accuracy thresholds and success criteria are defined upfront. No moving goalposts. No "directionally positive" deliverables. If a model does not beat the baseline, the engagement closes honestly — not with a softened narrative.
Not "improve efficiency." Not "leverage AI." A specific commercial outcome with a specific way to measure it. The number is agreed before work begins, so we know together whether we have succeeded.
Documented, handover-ready, runnable by your existing team after basic training. Nothing locked in one person's head. The engagement should leave your organisation more capable, not more dependent.
The methodology draws from an M.Sc. in Advanced Automotive Engineering at MUNER (Modena and Bologna), with academic and applied modules across Lamborghini, Ferrari, Pagani, Pirelli, Maserati, Ducati, and Robert Bosch GmbH. Industries where a model's error is not a presentation problem — it is a safety problem. That sensibility, applied to commercial work, is what we mean by engineering-grade rigour.
30–45 minutes. You describe the problem. We listen, ask, and tell you honestly whether we are the right people. No deck, no pitch.
Within two working days, a one-page brief outlining what we heard, what we would propose to do about it, the shape of the engagement, and the fee. Decision-ready.
Weekly cadence. Working sessions, not status meetings. Output draft halfway through so course-correction is possible before final.
Written deliverable, documented methodology, walkthroughs with the team that will own the output. The engagement ends when your team is ready, not when our invoice is paid.
The fastest way to know whether we are useful is a short, honest conversation about what you are trying to do — and what is in the way.