AI-powered retail planning.

How clients have engaged us.

• Prioritized AI use cases across demand, allocation, replenishment and markdowns.
• Redesigned planning workflows, decision rights and exception handling.
• Led vendor selection, program steering and commercial negotiations.
• Defined future roles, capacity and adoption plans around the work that remains.

Redesign the planning model.

• Connect assortment and demand with allocation, replenishment and markdowns.
• Define which decisions can be automated and which require planner approval or escalation.
• Size teams for future workload, exceptions and peaks; define required skills.

Select, build and connect.

• Compare vendor platforms, custom components and hybrid retail solutions.
• Test demand shifts, promotions, new products and stockouts in realistic pilots.
• Build planning components where they add value beyond vendor platforms.
• Connect product, sales and stock data with ERP, suppliers and commerce.

Turn automation into performance.

• Pilot with planners and trading teams, with explicit acceptance criteria and fallback procedures.
• Track forecast bias alongside availability, inventory, waste, markdowns and planning effort.
• Set ownership for data quality, model monitoring and changing business rules.
• Link rollout, training and workforce changes to proven operating performance.

• Vendor selection: tested retail scenarios, integration fit and total cost.
• Program steering: aligned business owners, vendors, integration and testing.
• Commercial negotiations: shaped pricing, scope and delivery commitments.