Deciding at the speed of demand.
In retail and consumer goods the problem is rarely missing data, it is the time between the signal and the decision. We work that interval, from reading demand to customer intelligence and the talent that keeps the store running.
Demand read at SKU and store level
A forecast that brings together history, promotion, calendar, weather and stockouts, with an explanation of what changed. The commercial lead sees the reason behind the number, not just the number.
Talent treated as a system
We built an AI-native talent management platform where recruitment, allocation and development run as one system, with agents doing the matching.
Customer intelligence at the right moment
An AI-native customer intelligence portal that puts context in front of sales and service teams when they need it, instead of scattered across tools nobody opens twice.
Enablement for the people deciding
We run an executive ways-of-working programme: a full day with leadership, who leave the room having decided where AI goes first.
The agent layer
Four agents in production.
Demand forecast
Predicts demand and flags stockouts early.
Replenishment
Proposes transfers between stores.
Talent matching
Matches need against available people.
Commercial analysis
Reads promotion, price and assortment.
Clients and cases
A leading food and beverage brand, an e-commerce group, and a data and AI company on the data side of the same problem.
What we built
An AI-native talent management platform and an AI-native customer intelligence portal, both in the client's own infrastructure.
Credentials
Data and AI leadership in global retail: Nike, H&M Group and Jumia.
The change
From reports that describe last week to agents that act in the next hour.