Industries
Retail
E-commerce platforms and retail fintech. More data than most sectors and less time to act on it — the useful work is about shortening the distance between a signal and a decision, starting from the path the money takes.
What I do here
- Demand forecasting
- Forecasting tied to the ordering decision it is meant to inform, so the output lands somewhere it changes behaviour.
- Customer operations
- Automating the high-volume, low-judgement half of customer contact while routing the rest to people quickly.
- Inventory and pricing
- Stock and pricing decisions supported by current data, with limits on what the system is allowed to change unattended.
What changes
- Stock decisions made on this week’s signal rather than last quarter’s
- Routine customer contact handled without a queue
- Margin protected by pricing that reacts within useful time
- Clear limits on what moves automatically and what needs a person
Work in this industry
4 written up — all case studies →- Fuel retail fintech
Selling AI engineers to a client who could already hire them
48-hour profilesThe operational promise that makes team extension credible
- Digital platforms
Proving a content migration lost nothing
3 dependent suitesAvailability, then metadata, then content — a failure upstream stops the noise downstream
- Healthcare services
The demo that admitted its AI was a rulebook
6 stagesConsultation, objection, agreement, payment, follow-up, admin
- E-commerce
Reading the money path first
Money path firstPayment, webhook and administrative boundaries reviewed before anything else
Other industries
Working in retail?
Thirty minutes, no deck. Tell me what is stuck and I will tell you whether it is the kind of problem I can move.