Make the business AI‑native. On your data, inside your walls
AI trained on your own data, working inside your own workflows, on systems you control.
Your infrastructure, your access rules
- Order book
- Production plan
- Line rate
- Supplier invoices
- Goods receipts
- Rate contracts
- Process parameters
- Material batches
- Shift log
Your model, trained on your data
Illustrative example. The planner asks: what is tomorrow’s dispatch risk? The model reads the order book, the production plan and the line rate, inside your own infrastructure, and answers: 3 of 41 orders at risk. Line 2 is running at 82% of plan; order 4471 needs 6 more hours.
Illustrative example. 39 supplier invoices arrive. Each is read and matched to its goods receipt and rate contract. 38 are matched. One, with a rate 4% above contract, is routed to a person for review.
Illustrative example. The quality engineer asks: why did rejections rise on line three last week? The model reads the process parameters, the material batches and the shift log, and answers: rejections rose with batch B-2291 on the night shift; barrel temperature ran 6 °C above setting.
Nothing leaves. Nothing is pooled.
What it looks like in a plant
- The planner asks what tomorrow’s dispatch risk is and gets an answer built from the order book, the plan and the line’s actual rate.
- Supplier invoices are read, matched to the goods receipt and the rate contract, and routed. A person sees only the exceptions.
- A quality engineer asks why rejections rose on line three last week, and the answer comes from process parameters, material batch and shift, not from a meeting.
- A quotation is drafted from the enquiry, the costing and the last twenty similar orders, ready for someone to approve.
- Maintenance is scheduled from the machine’s own history, and the spares order follows.
- Demand is sensed from orders, dealer stock and season, and the plan moves with it.
How we build it
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Trained on your data, and only yours
The models learn from your orders, your batches, your machines and your documents. Nothing is pooled with anyone else’s, and nobody else’s model learns from you.
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Running where you decide
On infrastructure you own or control, under your access rules. Your data does not leave the business to be processed somewhere else.
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Inside the workflow, judged on the number
AI is applied to a process that already works and measured by the same figure that process is measured by: hours removed, exceptions caught, dispatches made on time. No AI for its own sake, and no pilot that never reaches the floor.
The order of work
Foundations first.
- Connect the record
- Clean the data
- Train and deploy
Questions
Can you make our company AI-native?
Yes, in a defined order. First the processes run on one record, then the data is cleaned and connected, then we train AI on your own data and deploy it inside your workflows, on systems you control. The first automated workflows are built alongside the foundations, and each is measured on the process it serves.
Bring us one operating problem
Thirty minutes on the problem that costs you most. No deck, no demo.