Service
Know what will happen, before it does.
A predictive model takes the history already sitting in your systems and turns it into an estimate of what's coming: how much will be ordered, which machine will stop, which customer is about to leave and which deal will close.
Any recurring decision that leans on history.
In most organisations these decisions are made today from experience, spreadsheets and gut feel. A model doesn't replace the person deciding. It gives them a number to start from, with an explanation of where it came from.
For example:
- Demand forecasting for purchasing, production and inventory
- Failure prediction and maintenance for machines and production lines
- Spotting customers at risk of churning before they leave
- Lead scoring and win probability for open deals
- Flagging anomalies in orders, pricing or collections
History, data quality and one sharp question.
A model is exactly as good as the data it's fed. So the first step isn't the model, it's the mapping: what data exists, how many years back, at what quality, and who is allowed to reach it. That decides whether this takes weeks or months, and we tell you before we start.
- One business questionNot “predict everything”, but a specific decision the model should improve.
- A success metricForecast accuracy against what actually happened, and what that's worth in money.
- TransparencyEvery prediction comes with its reasoning, so it can be trusted or challenged.
Five steps, and each one earns the next.
- Diagnose
We go inside one department and pick the process that pays for itself fastest. And when there isn't one, we say so.
- Data, permissions & integration
We map what data exists across the ERP, the CRM and whatever comes back from the markets, at what quality, and who is allowed to reach it. That decides whether this is an eight-week build or an eight-month one, and we tell you before we start.
- Implement
We build the solution into the existing systems and run it with the real team, on the volume they handle every day.
- Prove
One metric, agreed before we start, measured against the day-one baseline.
- Expand, embed & monitor
Only once the number is in. The next process, with training and internal ownership.
- Permissions mapped before implementation
- No training on your data, and that is in writing
- Identifying information masked before anything reaches a model
- Data residency to your requirement, including GDPR
- A full log of what was sent, what came back and who approved it
Related
- Analytics and reportingReports arrive from every market in a different format and at a different pace, and by the time the picture comes together the decision has passed. We connect the data from the ERP, the CRM and the markets into one view, with a layer that explains what changed and why.
- Process automationOrder entry, CRM updates, quotes and reports that someone copies by hand. We take one process that costs you hours every week and rebuild it inside the systems you already run.
- The project isn't the AI.What happened at Cox, and why 75% of the work went where nobody budgets.
Tell us the process that costs you the most hours.
Twenty minutes with both of us. You'll leave knowing whether AI belongs in that process, including when the answer is no.