Retail runs on data that is always slightly out of date
Someone exports yesterday’s sales. They open a spreadsheet. They eyeball what is moving, guess at what to reorder, and place the order. By the time the stock arrives, the picture has changed again.
This is not a data problem. The data exists, in the POS, in real time. It is a workflow problem: nothing connects the data to the decision without a human in the middle doing arithmetic.
What we built
A POS platform where the analytics layer is not a report you open but a system that acts. Sales are read live, stock is projected forward, and reorder signals surface on their own rather than being derived by hand each week.
- Live sales analytics rather than end-of-day exports.
- Inventory projections that update as transactions happen.
- Reorder signals surfaced automatically, with the reasoning visible.
- Dashboards that answer the question rather than requiring interpretation.
Where the AI actually sits
Mostly, it does not. The majority of the value here came from a well-built data pipeline and a projection model. No language model required.
That is worth saying plainly, because plenty of agencies would have sold an AI layer here regardless. The honest answer is that this was an integration and modelling problem, and pretending otherwise would have cost the client money for no benefit. We reach for AI where the input is genuinely unstructured or the judgment is genuinely ambiguous. Inventory forecasting is neither.
The stack
Next.js and Recharts for the interface, NestJS and PostgreSQL for the pipeline. Boring by design.
Results
Manual effort down 70%. Decision speed doubled.