You have pilots, but struggle to get AI into production
Multiple PoCs exist in analytics or innovation teams, yet few are integrated into POS, eCommerce, or merchandising workflows.
Retail & eCommerce
AI-powered retail that moves the needle on margin, basket size, and loyalty
- Prioritise AI use cases by commercial impact, feasibility, and data readiness
- Move from PoCs to resilient, compliant AI in your core retail stack
- Blend fixed-scope projects with flexible squads that plug into your teams
Signals your retail organisation is ready for this
We’re a strong fit when you’re beyond experiments and need AI embedded into how your retail business runs day to day.
You want AI tied directly to P&L outcomes
You need initiatives that clearly move metrics like margin, sell-through, AOV, and service levels—not abstract innovation projects.
Your data is fragmented across retail systems
Customer, product, inventory, and transaction data live in silos across POS, ERP, WMS, and digital platforms, blocking advanced use cases.
You need to balance central standards with local agility
You operate multiple banners or regions and require shared AI capabilities with room for local pricing, assortment, and regulatory nuances.
You’re scaling teams and need specialist capacity
Your internal data and engineering teams are stretched, and you need experienced AI engineers and product builders who can plug in quickly.
You require robust governance and risk controls
You need AI that respects pricing rules, brand constraints, and regulatory requirements while remaining explainable to business stakeholders.
Retail AI in practice
Where we help retailers turn AI into measurable value
How we work with retailers
From fragmented pilots to a coherent AI retail platform
We partner with retail and eCommerce leaders to define the roadmap, stand up the data and MLOps foundations, and ship production AI that integrates with your core systems.
01
Discover value and prioritise the roadmap
We map your current retail stack, data assets, and strategic goals, then identify and rank AI use cases by P&L impact, feasibility, and time-to-value.
02
Design the data and AI foundations
We define the required data models, integrations, and governance, selecting architectures and tooling that fit your POS, ERP, eCommerce, and loyalty landscape.
03
Build and industrialise high-value use cases
We deliver fixed-scope solutions or embedded squads to build, test, and harden models and services, with CI/CD and MLOps to support continuous improvement.
04
Integrate with channels and operations
We connect AI services into your web and app frontends, store systems, merchandising workflows, and contact centres, focusing on adoption by merchandisers and operators.
05
Scale, measure, and optimise
We instrument KPIs like margin, sell-through, AOV, and service levels, then iterate on models, rules, and UX to scale successful patterns across banners and markets.
Business Outcomes
- Clear AI roadmap tied to revenue, margin, and cost levers.
- Faster path from PoC to stable production in your core retail stack.
- Reduced stockouts, markdowns, and operational waste.
- Improved customer experience and loyalty across channels.
- Flexible mix of fixed-price delivery and dedicated squads as you scale.
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