Platform · Discover Freshflow

Theintelligencelayerforfresh

Not a forecast widget. A full ordering decision system for fresh. Inventory simulation, demand forecasting, replenishment optimization — all integrated in a store-floor Co-Pilot. Built for the only category in the building that breaks every generic system.

CO-PILOT · IN-STORE
What you get

Measurable outcomes, per store.

up to30%less waste

Per store, measured vs. baseline.

up to4%more revenue

More on the shelf when it matters.

93%acceptance rate

of the AI's order proposals are accepted by staff.

Fresher products.

Less time on shelf, better quality in the basket.

Independent of skilled labour.

Trainee or veteran — same result on the shelf.

Why fresh breaks generic systems

Six ways fresh produce breaks a generic replenishment system.

01

Inventory inconsistencies

The ERP says 10 bananas. Only 4 are saleable. Without inventory truth, every order is a guess.

02

PLU chaos

5 apple varieties, 1 code. Organic gets mis-scanned as conventional. The data foundation is broken — and so is every forecast built on it.

03

Variable shelf life

Strawberries: 1–3 days. Potatoes: weeks. A system that treats both the same hasn’t understood freshness.

04

Staff turnover

The experienced clerk retires. The trainee takes over. And waste doubles. Every week.

05

Weather, season, promotions

30°C and watermelons fly off the shelf. Rain and nothing moves. Add promotions that cannibalize neighboring SKUs. Different every day.

06

Local suppliers

Up to 50% of goods come from local suppliers — without clean goods receipts. Half your inventory is flying blind.

The four components

Freshflow works as a whole system.

Inventory simulation, forecasting, replenishment optimization and the Co-Pilot. They map to the four jobs every fresh-produce team does every morning — and they pass state between each other every second.

01Saleable inventory, not ERP inventory

Saleable inventory you can actually trust.

Freshflow doesn't use your ERP stock as a starting point. It builds its own inventory model — simulating what remains on the shelf, what's still sellable, and how quality will degrade before the next delivery.

  • Shelf-life-aware: tracks sellable kg, not just nominal stock.
  • Continuously calibrated against POS, waste and weather.
  • Per-SKU degradation curves, learned from your stores.

Saleable inventory · Live

Our AI inventory model · running per item, per store

What ERP says
Adjusted for what ERP can't see
  • Perishability & quality drift
  • Scan & PLU inconsistencies
  • In-store transformations
  • Expected demand to next delivery
What's actually sellable
02Demand modelled as a range, per store, per day

Forecast what's likely, not what's average.

Weather, seasonality, promotions, regional events, stockout-corrected history. Freshflow treats demand as a distribution — because in fresh, being wrong in one direction costs very differently than the other.

  • Weather, seasonality, promotions and regional events, modelled per store.
  • History corrected for past stockouts — so you don't under-forecast forever.
  • Outputs a full demand distribution, not a single point estimate.

Forecast · Strawberries 500g

Wed · likely vs. safe demand · signals modelled per store

Weather+6°C ↑
SeasonWeek 19 · peak
Promo−0,50 €
RegionLocal fair Sat

Range = P50–P90 · 9-in-10 confidence

03The order that maximises profit, not the one that hides stockouts

Order to margin, not to a service level.

The system balances the asymmetric cost of under- and over-ordering for each item, each day. Strawberries and bananas don't have the same economics. Freshflow knows the difference.

  • Per-SKU under- and over-order penalties — not one target service level.
  • Shelf life, margin and supplier MOQ all priced into the decision.
  • The order that maximises margin — not the order that hides stockouts.

Asymmetric economics · Per item

Same forecast distribution. Two different orders.

StrawberriesOver-ordering kills the margin.Short shelf lifeOrder below the mean
BananasUnder-ordering kills the sale.Fast moverOrder above the mean

Forecast accuracy isn't the goal. Profit per item is.

04Automated, profit-optimal replenishment for every fresh SKU

Place the right order, faster.

Each morning, the Co-Pilot presents your team with order proposals. Your staff review only the exceptions.

  • Exception-first interface — review only what needs your judgment.
  • One-tap approve, one-tap adjust, full audit trail.
  • Runs on the tablet you already have in the back office.
Freshflow Simulator

Freshflow Simulator. Decide with foresight.

Change a rule. See the impact — before you commit. The Simulator previews the expected change in waste, availability and revenue, across your fresh aisle, in real time. Transparency over a black box.

01 · Set a rule

Tighten safety stock. Push availability higher on the categories you compete on. Loosen it where waste is biting.

02 · See the impact
Waste
Availability
Revenue
03 · Decide

Adopt. Adjust. Or roll back. You stay in control.

✓ Adopt↺ Roll back
Freshflow compared

Standard vs. specialized.

A generic auto-replenishment system and Freshflow look superficially similar. Pull the cover off and they're built on completely different assumptions — here's where, line by line.

Standard auto-replenishmentGeneric ERP / dry-grocery
FreshflowBuilt for fresh produce
01Data foundation
Historical sales
120+ data points, context-aware
02Inventory
ERP stock = truth
Saleable inventory, simulated
03Shelf life
Static or ignored
Probabilistic spoilage curves
04PLU / item chaos
Not addressed
Dedicated algorithm, retailer-specific training
05Local suppliers
Blind spot
Dedicated module with supplier selection
06Order logic
Forecast = order quantity
Gross-profit optimization under uncertainty
07Suggestions accepted as-is
< 50%
93%
08Focus
Dry assortment
5 years exclusively fresh
Build vs. buy

Yes, you could build this internally. Here's what it actually takes.

Most retailers underestimate the stack required to turn the messy reality of a fresh aisle into the right order quantity. A forecast alone is one ingredient. Here's the whole recipe.

A

Saleable-inventory truth

Not what ERP says — what's actually on the shelf, sellable, and not yet spoiled.

B

Demand reconstruction

Past stockouts hid demand. We rebuild what would have sold if the shelf had been full.

C

Probabilistic demand

Demand is a distribution, not a point. Modelling it that way is the whole game.

D

Context-aware features

Weather, seasonality, promotions, cannibalisation, halo — all priced in per item.

E

Per-SKU economic optimization

Asymmetric over- vs. under-order cost. Strawberries and bananas don't share a service level.

F

Substitution logic

An SKU stockout isn't always a category stockout. The math has to know the difference.

G

Continuous retraining

Drift detection, monitoring, recalibration — every day. Without it the model rots.

H

Freshflow App

Built so produce staff actually adopt it. Without the app, the model is just an opinion.

Each one alone is a multi-quarter project. Together they're a category-leading product, five years in.

Data we work with

Standard exports in. Store-specific models out.

No middleware, no migration, no new infrastructure. Freshflow ingests the same exports your warehouse and POS already produce — and gives you back per-store models in days.

IN

What we take

  • Your existing sales, stock & delivery data
  • Optional local context
+ WE ENRICH WITH

Context, layered in

  • Weather
  • Local events & holidays
  • Promotions & price changes
  • Cannibalisation & halo
  • Country of origin
  • Supplier constraints
FRESHFLOW

The engine

Inventory simulation · Forecasting · Replenishment optimization · Co-Pilot

Security & deployment

Where data lives. How it stays compliant.

EU-hosted by default. GDPR/DSGVO compliant. Retraining cadence and audit trail documented for every customer.

Where data lives

EU regions only.

All customer data is processed and stored in EU regions. No data leaves the region without explicit contract.

Compliance

GDPR / DSGVO.

Full data-processing agreement. Right-to-erasure honoured at the store and SKU level.

Model retraining

Weekly cadence.

Per-store models retrained weekly on rolling data. Drift detection runs daily; models can be paused per SKU.

Audit trail

Every decision logged.

Every order proposal, override and approval is logged with timestamp and signer. Exportable on request.

From contract to first results

Live in weeks. No infrastructure changes.

The five stages every roll-out follows. Most customers see measurable waste reduction shortly after go-live.

01 · Alignment

Scope & stores selected.

Goals confirmed with operations. Scope defined. Data export feasibility checked.

02 · Decision call

Data sample reviewed.

Sample exports validated. Initial signal mapping. Demo against your real store.

03 · Go decision

Contracts & DPA.

Contract signed. Data-processing agreement countersigned. Kick-off scheduled.

04 · Implementation

Models trained, app deployed.

Per-store models trained on historical data. Freshflow App deployed to back-office tablets.

05 · Rollout

Go live · shadow week.

One shadow week alongside existing process, then full Co-Pilot. Field team on the floor.

Most customers see measurable margin improvements within 1–2 months after go-live.

Book a demo

Fresh deserves its own intelligence.

We'll show you what the system looks like in a store similar to yours — and what results you should realistically expect.

Talk with our Experts Estimate your impact
Duration30 minutes
Time to first results6–8 weeks
Setup effortWeeks, not months
Infrastructure changesLightweight
Live acrossEurope