Forecasting, inventory and supplier risk you can act on, built on data your planners trust.
Most supply chain teams already own the raw material for better decisions. Demand history sits in the ERP, orders in the planning tool, supplier records somewhere else again, and the freight picture in a spreadsheet someone updates on a Friday. The shortage is rarely data. It is that the data lives in pieces, so forecasts, stock decisions and supplier calls get made on partial views.
We build AI for supply chain and logistics the way we build everything: foundation first. Before any model touches a forecast, we connect procurement, demand, supplier and inventory data into one governed picture. AI built on that footing improves forecasting accuracy, highlights exposure and gives you a clearer view of material flow. AI built without it produces confident answers to the wrong numbers.
This page is for the people who carry the outcome: heads of supply chain, logistics and operations directors, planners, and the IT leaders who keep their systems running.
Forecasts built on connected procurement, demand, supplier and inventory data, rather than one system's partial view of the world.
A clearer picture of material flow and stock exposure, so excess and shortage stop arriving as surprises.
Supplier risk highlighted while there is still time to act, before it lands on service levels or cost.
Anticipate disruption instead of reacting to it, and adjust orders and stock ahead of the impact.
Procurement, demand and inventory data reconciled into a single version of the truth that planners and the board can share.
Outputs land in the tools your team already uses. Insight that sits in a separate dashboard tends to stay there.
We were amazed at how quickly we started seeing results. In the first 3 months, we'd engaged in over 1,500 conversations and closed 2 net new opportunities that were initiated by 'SlimGPT'.
We start with your operation, not your technology. Where does cost, risk or inefficiency sit, and which of it can data actually reach?
We assess how your data is structured, where it lives and how reliable it is, then design the foundation that makes your planning and procurement systems usable for AI.
Initiatives are chosen for business value, not novelty. Each candidate is weighed for financial impact, feasibility and scalability.
We build into your real workflows and existing systems, not a test environment your team has to remember to visit.
Successful use cases are designed for roll-out from the start, then repeated across sites, regions and product lines with shared metrics and governance.
Models drift and businesses change. We monitor, refine and support after go-live so performance holds.
No. We design AI to work with your existing environment. Systems like your ERP, warehouse management and planning platforms remain the foundation. AI improves how the information in them is used, not where it lives.
Very little by design. Most data work and system integration runs in parallel to daily operations, and live deployment is managed carefully around your planning cycles.
It is the normal starting point, and it is exactly what the data foundation stage exists to fix. We assess what you have, where it lives and how reliable it is before anything gets built on top.
Forecasting, inventory and supplier risk are the common starting points, because they combine high cost exposure with data most organisations already hold.
A short conversation. It is usually enough to work out where value is most likely, what the obstacles are and what a practical roadmap looks like.
If you carry responsibility for service levels, stock or supplier performance, the question is rarely whether AI could help. It is whether your data is in a state to support it. Talk to us and we will give you a straight answer on both.
Tell us where your data is today and what you want AI to do. We will come back with a straight answer on what your foundation needs and where the quickest real win is.
Talk to us