Retail and commerce teams live by a calendar: a few trading days decide the year, and the site has to hold while the business keeps experimenting. We help you ship fast, test what matters and go into peak knowing what will break.
The question we hear most
The hard part of peak is not the peak. It is the fifty weeks of change before it — prices, promotions and journeys altered every week, each one a new way for the busiest day of the year to go wrong.
We plan backwards from the trading calendar: find the journeys that carry the revenue, test them under realistic load, and keep weekly releases safe with regression ranked by risk rather than run in full. The aim is not a frozen site in November, but one that can keep changing right up to the event.
The shifts changing what good delivery looks like here, and what each one asks of the teams doing the work.
Black Friday, Diwali, Singles’ Day and a retailer’s own sale events concentrate trading into a handful of days.
What it means for deliveryPerformance and resilience work has to be scheduled backwards from the trading calendar.
Quick commerce and same-day delivery have turned stock accuracy and delivery promises into a real-time problem.
What it means for deliveryInventory and order events need to flow continuously, not in nightly batches.
Shoppers increasingly start with natural-language search, and expect assistants that know the catalogue and the returns policy.
What it means for deliveryAssistants have to answer from your own content, with guardrails on prices and promises.
Consent rules, the European Accessibility Act and price-reduction rules shape what a checkout or a promotion may do.
What it means for deliveryCompliance checks belong in the regression suite, not a pre-launch review.
The pressures differ inside an industry too. These are the parts of it our teams work in, and what sets each one apart.
Seasonal ranges, heavy promotion calendars and high return rates.
Thin margins, real-time stock and delivery promises measured in minutes.
Seller onboarding, catalogue quality and traffic that scales with every campaign.
Brands selling direct while keeping wholesale and marketplace channels in step.
High-value orders, financing at checkout and complex product data.
Stores, web and app sharing one view of stock, orders and customers.
Each offering runs on its own or as part of a wider programme, and draws on the AI services, consulting practices and products listed with it.
The journeys that carry the revenue, tested under realistic load early enough to fix what the first test finds.
Regression ranked by risk and rollouts behind feature flags, so pricing, promotion and journey changes keep shipping without gambling on the peak.
Orders, stock and customers kept consistent across store, web, app and marketplace systems.
Assistants grounded in your catalogue and policies, and product content enriched with a person approving what goes live.
Client storyCommerce platform in 14 countries
Client name withheld under NDA. Figures are from the client’s own reporting.
We do not certify compliance — your auditors and assessors do. We make sure the evidence each framework asks for is produced by the delivery process itself.
Common first engagements in this sector. Most begin with a two-week, fixed-price discovery that leaves you with a costed plan whatever you decide next.
Early enough to fix what the first load test finds. We start with the journeys that carry the most revenue, test them under realistic load and work back from the trading date.
No. We integrate with the systems you already run and add the traceability, automation and data layer around them.
Yes. Our approach is zero training on your data, with private model routing for hosted models. Where the data cannot leave your control at all, we build the whole stack on your own hardware, air-gapped if required.
Yes, though the plan changes. With weeks rather than months, we focus on the few journeys that carry most of the revenue, load-test those first and agree a change freeze ranked by risk — then fix what the tests find before anything else.
Most peak-day failures were visible weeks earlier to anyone testing the right thing. How to build a load model from real traffic, test the dependencies you do not own, and decide what to switch off before it breaks.
The time to find out what a service is doing is not the night it fails. What to instrument from day one, how to choose alerts people will not learn to ignore, and why service level objectives settle arguments.
Tell us the constraint and the systems behind it. We will put an engineer who has worked with both on the call, not the nearest salesperson.
What the first call covers
