Now in early access

Physical retail's equivalent
of ecommerce analytics.
Finally.

Upload your floor plan, map shelves to categories, import sales data, and get a data-driven Space Allocation proposal — guided step by step in one workspace.

See it in action

Floor plan image + sales CSV/Excel.

The Problem

You manage 400 stores.
You can see zero of them.

Ecommerce teams know their top products, best pages, and worst-converting flows in real time. Your physical stores are a black box — until the monthly P&L arrives.

Tribal knowledge walks out

3 people in your company know which store layouts work. When they leave, that knowledge disappears. No documentation. No data. No way to replicate it.

Problems compound silently

A Coffee & Tea zone earning €28/m² instead of €74/m² looks fine in the weekly report. That's €38k/month in missed revenue — per store — compounding while you look elsewhere.

Layout decisions are guesses

Every floor change is a physical experiment. Roll it out in 20 stores. Wait 6 weeks. Analyse the data manually. By then, the context is gone and you've already moved on.

Features

A guided workspace
from floor plan to proposal.

Seven onboarding steps in one app — upload your layout, map shelves, import sales, run baseline analytics, and generate a Space Allocation proposal.

01 Guided store setup

Digitize your store in seven guided steps

Upload a floor plan and calibrate scale, add shelf photos, import sales from CSV or Excel, define shelf boxes (manually or via detection), then place shelves on the layout — all before analytics run.

  • Floor plan upload with px/m scale calibration & grid
  • Shelf photos for computer-vision or manual shelf definition
  • Sales upload with assisted column mapping
  • Drag-and-drop shelf placement with rotation on the floor plan
Onboarding — guided setup
1 Floor plan setup In progress
2 Shelf photos
3 Sales upload
4 Shelf definition
5 Shelf placement
6 Baseline analytics
7 Space Allocation proposal
Scale calibration
41.31 px/m
Grid cell: 0.1 m
Store area
51 m²
Floor plan uploaded
Optimization proposal — category targets
Category Current Proposed Δ margin
Chocolate 6.0 m² 5.2 m² −€840
Crisps 3.0 m² 3.8 m² +€2.1k
Gummy Bears 1.0 m² 1.0 m²
Projected margin
€48.2k
+€1.3k vs baseline
Model
Fair Share
Shelf assignment: best target match
02 Baseline analytics & Space Allocation

Baseline first, then a data-driven space proposal

Step 6 summarizes margin, turnover, and exposure area by category. Step 7 runs Space Allocation — Fair Share by margin share or power models with diminishing returns — and assigns targets to real shelf bays.

  • Baseline KPIs per category before any changes
  • Fair Share and elasticity-aware allocation models
  • Current vs proposed category overlay on the floor plan
  • Shelf assignment maps category targets to bays
03 Portfolio Dashboard

Compare stores and spot the biggest space allocation wins

Run Space Allocation per store, then roll up to a chain-wide view. See which locations have the highest projected margin uplift and how category exposure shifts across your network.

  • Portfolio KPIs: turnover, margin improvement, space utilization
  • Top stores ranked by optimization opportunity
  • Store × category matrix: current vs proposed m²
Dashboard — 12 stores analyzed
Avg margin uplift
+8.4%
Total turnover
€4.2M
Top opportunity stores
Amsterdam Central
+€18k
Rotterdam Blaak
+€12k
Utrecht HC
+€9k
Store Category Allocation matrix · expand / reduce by category per location
04 Method guide

Transparent equations, not a black box

In-app method guide explains how baseline metrics and Space Allocation models work — Fair Share, power curves, and shelf assignment logic — so merchandising and finance teams can trust the numbers.

05 Project export & import

Share work across stores and teammates

Export a complete project as a ZIP — floor plan, shelf definitions, sales mapping, and optimization state. Import it elsewhere to pick up where you left off or replicate setup across locations.

How It Works

Live in 2 hours.
No IT department required.

CSV upload of the files you already have. No API keys, no integrations, no security review.

Step 01 — Day 1, Hour 0

Upload your existing files

Export POS data as CSV. Drop in your floor plan — PDF, photo, or image. That's it. No schemas, no mapping, no IT ticket.

Drop files here
pos_export_q4.csv, store_layout.pdf
CSV PDF PNG/JPG No IT needed
Processing — Amsterdam Central
Floor plan digitised 2 min
POS data mapped to zones 18 min
Revenue per m² calculated 34 min
Heatmap ready — go live 1h 58m
Step 02 — Day 1, Hour 2

ML does the heavy lifting

Our pipeline digitises your floor plan, maps transaction data to zones, and calculates revenue density. First heatmap is live in under 2 hours.

Step 03 — Ongoing

Act on intelligence, not instinct

ML alerts surface within 24 hours of each upload. Your team gets answers in a dashboard, not a data warehouse. Share board-ready PDFs in seconds.

€38k
revenue recovered
in first month
3.2x
average ROI
in 90-day POC
<10s
board-ready
PDF export
2h
time to first
live insight
Results

Numbers that matter to a COO

0%
avg revenue/m² uplift within 90 days
0h
time to first live heatmap
€0
hardware investment required

Every month without this
is margin you don't get back.

Upload your first store's POS data and floor plan today. First heatmap in 2 hours. No IT department, no security review, no hardware.

No IT involvement. Works with your existing POS export.

Zero hardware
Works with what you already have — POS exports and floor plan images
No IT involvement
CSV upload bypasses 6-month security reviews
Risk-free POC
90-day POC at ~90% discount. Cancel anytime.