Service
One picture, on time, instead of ten files.
Reports 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.
From raw data to the management report.
Most of the work isn't the chart, it's what happens before it: collecting data from different systems, aligning definitions, cleaning and validating. After that, AI can do what's hard to do by hand: read unstructured reports, flag anomalies and explain them in plain language.
- Sales and inventory reports from distributors and markets, merged
- Management reports that update themselves instead of being rebuilt every month
- Alerts on anomalies in sales, inventory or collections
- Questions about the data in plain language, with answers that point to the source
Because it starts from the dashboard, not the definitions.
A beautiful dashboard on numbers nobody trusts changes no decisions. Before building anything, we agree what counts as a customer, a sale or a return in each system, and who owns each number. It's the least glamorous part, and it's the part that decides.
- Shared definitionsThe same number means the same thing in every report and every market.
- OwnershipEvery metric has a person responsible for it.
- TraceabilityEvery number in a report can be traced back to its source.
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
- Predictive modelsA 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.
- Internal assistantsThe answer already exists somewhere: in a procedure, a spec sheet, a customer file or a veteran's head. An internal assistant connects that knowledge to your teams, answers in plain language and shows where the answer came from.
- 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.