OTB SaaS with AI
An Open-To-Buy planning SaaS for fashion retail: it turns the operation's data into a calculated buying matrix, a diagnosis and an action plan — the numbers from a deterministic engine, the reading and prioritization from AI.
In fashion retail, buying well means deciding how much to buy, of what and for when — balancing projected sales, current stock, receipts still on the way and a safety margin, category by category and week by week. That is the Open-To-Buy calculation: the amount left to spend without overstocking. And it's a moving target, shaped by seasonality, margin, coverage, history and the operation's commercial calendar.
OTB Flow, a system built by WiseMage, was made to own that decision. It starts from the data the operation already produces and turns it into a single structure for analysis: what to buy, what is piling up and where the operation is exposed — instead of a spreadsheet each person reads their own way.
The data exists, but scattered: it leaves the ERP in one shape, gets patched together in a spreadsheet, and each category ends up calculated its own way. Without a consistent base, OTB becomes a manual estimate, reading stock and coverage depends on whoever built the tab, and there's no way to compare store with store or period with period.
The real problem isn't the spreadsheet itself — it's turning what the operation generates into something reliable: normalizing the data, calculating OTB by the same rule every time, seeing overstock and stockouts, pointing to what to buy, what to liquidate and what to redistribute, and turning that into actions you can track. That whole path was what was missing.
OTB Flow is a multi-tenant SaaS platform built from scratch: each operation runs with its own isolated analytics database, and the flow goes from data entry all the way to the tracked action. It starts at import — the spreadsheet exported from the ERP is read, its columns are mapped to the system's model with AI assistance, then validated and normalized before any calculation.
From there, the calculating is done by a deterministic engine. The OTB matrix — sales projection, stock rolling week by week, coverage and the buying amount per category and period — is the single source of the numbers. AI comes in afterwards, on top of those already-calculated numbers, to read the scenario, prioritize and write the diagnosis and the action plan. The numbers are the system's; the reading and the prioritization are the AI's.
System captures with demonstration data.
The gain isn't a sales figure — it's the whole path in one place. The operation's raw data comes in, gets normalized and calculated by the same rule, OTB stops being a manual estimate, and analysis becomes diagnosis, priority and a trackable action. What used to mean building a spreadsheet and eyeballing it now comes out structured, comparable across stores and periods, and ready to become a buying, liquidation and redistribution decision.