Fresh produce changes every hour. Retail decisions should too.
Departmint helps fresh retailers identify inventory that may need attention, and improve the timing of inspections and markdown decisions — before value becomes waste.
The goal isn't to discount more. It's to intervene better.
Every batch of fresh produce has a window in which action still recovers value. Departmint is being built to help retailers find it.
Markdown healthy inventory
Inspect or mark down at the right moment
Deterioration
Retail systems are excellent at what happened. Fresh needs a decision about what happens next.
Fresh produce is unusual inventory: its physical condition changes continuously, and its commercial value changes with it. Inventory and POS systems answer what arrived, what sold and what remains — with precision.
The harder question is continuous, and today it is usually answered by whoever is on shift: what should we do with what remains, and when? That decision is made periodically, manually, and with limited economic information behind it.
From retail data to a decision.
POS / sales velocity
Markdown history
Waste history
Product observations
Batch & arrival information
Temperature, where available
WHY?
WHEN?
WHAT SHOULD I DO?
Then automate it.
We are not building complicated AI first and looking for a problem afterwards. The order is deliberate: start from the retail decision and its economic outcome, learn the workflow and the data around it, and only then automate — because a model is only worth building once you know precisely what it must predict.
RECOMMENDATION → AUTOMATION → VISION → CONDITION INTELLIGENCE
One recommendation, with the reasoning attached.
PRODUCT VISION — SIMULATED DATAWhether staff accepted the recommendation, whether the product sold, how much margin was recovered, whether the intervention was unnecessary — each answer is a labelled example that makes the next recommendation better.
A margin product with a sustainability consequence — in that order.
Protect margin
Avoid unnecessary or poorly timed markdowns. A discount given to product that would have sold at full price is a permanent margin loss that no waste report will ever show you.
Recover value
Intervene while the action still works — before inventory reaches the point where no price clears it and the entire margin is written off.
Reduce waste
Less avoidable disposal, as a consequence of better commercial decisions rather than as the goal itself. Waste falls because the decisions improved — not because everything was discounted.
Start with one decision.
No retailer needs to transform its fresh department on day one. The first deployment is deliberately narrow enough that the result can be attributed with confidence.
Berries are a plausible starting candidate, not a decision. We look for categories with meaningful shrink value, frequent markdowns, sufficient volume, real margin exposure, a workable intervention window, and outcomes that can actually be measured.
Lower waste is not enough.
Any retailer can reduce waste to nearly zero by discounting everything aggressively — and destroy the category doing it. The intervention has to beat the existing process on economics, not on tonnage.
Flagging healthy product for inspection or markdown costs staff time and margin every time it happens. So the question the engine has to answer is not "is this product deteriorating?" — it is the harder one:
Built for the people who own fresh performance.
Head of Fresh
Produce Director
Fresh Category Manager
Retail Operations
These figures describe waste generated at retail level. They exclude household waste and upstream supply-chain loss, which are larger and are not the problem we address. Retail waste is the smaller share — but it is the share a retailer directly controls, books as shrink, and can act on within a single shift.
RETAIL FOOD WASTE IN THE GCC”, MARCH 2025.
FIGURES AS PUBLISHED.
Built for the realities of GCC fresh retail.
Fresh produce can travel through multiple commercial stages before it reaches a GCC shelf. Departmint is being designed to help retailers understand what action makes sense once that inventory is in their hands.
Built as enterprise B2B software.
Controlled retailer pilot
A narrow, instrumented deployment. Commercial terms agreed with the design partner.
Annual software subscription
Priced by store, module and category once the value is understood.
Multi-store chain agreement
With integrations, analytics, implementation and enterprise support alongside.
Pilot programmes available for selected design partners.
The wedge is small. The platform is not.
ROADMAP — NOT BUILT TODAYEach deployment could eventually relate product, origin, condition, time, temperature, price, demand, intervention, sell-through and waste. That relationship is the eventual defensibility — and it does not exist yet. We would rather call it a potential flywheel than a moat.
A real category, with a specific wedge inside it.
Companies including Smartway, Wasteless, Freshflow, OneThird, Clarifresh and Focal Systems are working on parts of this problem — dynamic markdown, demand forecasting, freshness sensing, shelf monitoring. Their existence is evidence that the category is real.
We are not claiming to be first, and we are not competing on a feature grid. Departmint is testing a specific intersection, starting in GCC fresh retail.
We're looking for GCC retail design partners.
We are currently speaking with fresh-category and retail-operations teams to validate the first Departmint workflow. The first pilot is intentionally narrow — one retailer, one store, one category, one measurable decision.
UAE or Saudi Arabia
Meaningful fresh-produce operation
Willing to share relevant historical data
Interested in testing fresh-category economics
We are pre-seed and pre-product, and we would rather you heard it here.
No revenue or ARR
No accuracy or computer-vision performance
No waste-reduction or margin results
No proprietary dataset
No store deployments
No validated pricing
A solo founder — an industrialization and design engineer with a background in complex physical systems, product development and entrepreneurship — building at the intersection of AI and retail operations.
CV/ML engineering and fresh-retail operating expertise are planned as key hires and advisors immediately after initial customer validation. Hiring them before knowing which decision creates value would be building a capability in search of a problem.
Every dashboard on this page is simulated. Every forward-looking capability is labelled as roadmap, concept or hypothesis.
Building the intelligence layer for perishable retail.
Departmint is at pre-seed stage and building its first GCC retail design partnerships. If you invest in retail technology, food technology, enterprise AI, waste reduction or GCC technology, we are happy to share the full thesis and pilot design.