Forecasts nobody trusts
A central model that cannot explain itself gets overridden into irrelevance by week three.
Industry Practice
Retail
Decisions at the speed of the floor.
Field Context
The work on the floor
Retail margins live in availability, labour, and the last metre of the aisle. We build systems that help merchants, store managers, and planners act on the same picture — without asking them to become data scientists.
What we hear
The constraints we are hired to remove.
On-shelf reality
The system says in-stock. The shelf is empty, or the facings are wrong, and no one sees it until a mystery shop.
Labour against traffic
Rosters follow last year’s curve. This week’s weather, promo, and local event do not.
What we build
Systems, not a catalogue of models.
Demand and replenishment
Store-item forecasts with promo and weather, plus a planner workflow that records every override.
Shelf and planogram vision
Out-of-stock, misplaced, and pricing exceptions from existing cameras — routed to the associate, not a dashboard graveyard.
Workforce and tasking
Traffic-aware labour and a task list that follows the real work of the day, not a static checklist.
Clienteling copilots
Assisted selling grounded in stock, size curves, and the brand’s own voice — never a generic chatbot.
If it is working
What a good engagement looks like from the inside.
- Put a forecast in the buying meeting that can be challenged
- See true on-shelf availability, not system stock
- Match labour to the hours that actually convert
- Give associates a tool they will open on the floor
Also in the practice
Start a conversation
Tell us about the operation you want to improve.
A plant, a banner, a brand, a pathway. We will tell you honestly whether AI is the right lever — and what it would take to put it in production.