Retail & E-Commerce

Peak readiness is treated as a programme in its own right, not a pre-go-live test. It combines infrastructure right-sizing and auto-scaling validation, realistic load testing with traffic profiles based on historical peak data, chaos engineering to verify failure modes under load, and operational readiness runbooks so the engineering team knows exactly what to do if any component degrades. We typically run progressive load tests from 50% to 150% of projected peak, with synthetic monitoring in production from the week before peak trading begins. The objective is to reach peak with confidence based on evidence, not optimism.

The answer depends heavily on the scope of the existing platform, the number of integrations, the complexity of the catalogue, and how much customisation exists in the legacy system. A mid-scale retailer migrating a monolithic commerce platform to a composable architecture typically takes twelve to twenty-four months end-to-end, including discovery, architecture design, phased build, and cutover. We avoid big-bang cutovers where possible—using strangler-fig or traffic splitting approaches to validate the new platform under real production load before the legacy system is decommissioned, which reduces migration risk significantly.

Yes. Retail technology programmes are rarely purely technical—they involve trading, merchandising, marketing, and operations stakeholders whose requirements shape what gets built. Our retail programme leads are comfortable working across these stakeholder groups and translating between trading requirements and engineering decisions. We run structured discovery workshops that bring commercial and technical stakeholders together to build a shared understanding of priorities before delivery begins—reducing the risk of building the wrong thing technically correctly.

We design OMS architecture around event-driven order state machines—each order state transition is an event published to a durable message bus, which allows fulfilment nodes to subscribe to the events relevant to them independently, and provides a complete, auditable order history as a natural by-product. Inventory reservation is handled through distributed reservation patterns that guarantee no overselling without introducing synchronous bottlenecks. For existing retailers with incumbent ERP or WMS systems, we design integration layers that allow the new OMS to coexist with legacy systems during a phased migration.

Personalisation ROI is measured through controlled A/B experiments—holdout groups receiving non-personalised experiences versus groups receiving personalised recommendations—with clear primary metrics (conversion rate, average order value, basket size) and guardrail metrics (returns rate, customer service contacts) to catch any negative effects. We build the A/B testing framework into the platform itself so experiments are cheap to run and results are statistically rigorous before any changes are made permanent. This also provides ongoing measurement capability beyond the initial build, allowing the trading team to continue optimising without engineering intervention for each test.