Retail & Commerce
Omnichannel that holds up on the busiest day.
Commerce platforms, order management, and store systems built for peak trading — where an hour of degraded checkout is measured directly in lost revenue.
35+
Retail programmes
12x
Peak traffic handled
1.8s
Median page load
27%
Conversion uplift
The pressure
What retail & commerce teams are actually dealing with
Peak days that break the year's architecture
A platform that runs comfortably for eleven months and falls over on the one trading day the annual number depends on.
Inventory that disagrees with itself
Stock figures that differ between the website, the warehouse, and the store, producing cancelled orders and refund costs.
Channels built as separate businesses
Online, store, and marketplace operating on separate systems, so a customer who buys in one and returns in another becomes a manual process.
Platform upgrades that stall roadmaps
Heavily customised commerce suites where every vendor upgrade turns into a six-month project with no customer-visible benefit.
What we build
What we build for retailers
Composable where it earns flexibility, monolithic where it saves money — decided per capability, not by fashion.
Commerce platforms
Headless and composable storefronts built for peak load, with performance budgets enforced in CI rather than checked before Black Friday.
- Load-tested to peak
- Performance budgets
- Progressive rollout
Order management
A single order lifecycle across channels, with sourcing rules that balance delivery promise against fulfilment cost.
- Distributed sourcing
- Split shipments
- Unified returns
Inventory & availability
One real-time stock position across warehouse, store, and in-transit, with safety buffers tuned per channel.
- Real-time availability
- Store stock visibility
- Reservation handling
Store systems
POS integration, clienteling, and fulfilment-from-store tooling designed for staff working on the shop floor, not at a desk.
- POS integration
- Ship-from-store
- Offline-tolerant
Personalisation & search
Merchandising, search relevance, and recommendations that merchandisers can tune themselves without a deployment.
- Merchandiser controls
- Relevance tuning
- Experimentation framework
Commerce analytics
Trading dashboards with a single definition of revenue, margin, and returns that finance and trading both accept.
- Unified trading view
- Margin after returns
- Cohort retention
Compliance
The regulatory surface we build against
Controls are designed into the architecture and evidenced in the pipeline, so an audit is a report rather than a project.
Outcomes
What these engagements produced
Representative results from engagements in this sector.
- 01
Fashion retailer
Twelve times normal traffic, no degradation
Re-platformed the storefront with an enforced performance budget and load-tested to 12× the previous peak before the trading event.
- 02
Multi-brand group
Order cancellations cut by two thirds
A single real-time inventory position across 140 stores and two warehouses removed the overselling that drove most cancellations.
- 03
Grocery chain
Ship-from-store live across 90 locations
Store fulfilment tooling built with store staff over six weeks of on-site iteration, then rolled out in three waves.
How we deliver here
Three things that make retail delivery different
Peak
The year is decided in a handful of hours
A platform that handles the annual average comfortably can still fail at the moment that matters. We size against the trading event, load-test to a multiple of the previous peak, and enforce performance budgets in CI so headroom is not quietly eroded between events.
- Load tested to a multiple of last peak, not to it
- Performance budgets gating every pull request
- Graceful degradation designed for the checkout path
- Freeze window and war-room protocol agreed in advance
Payments
Keep cardholder data out of scope wherever possible
The cheapest way to pass a PCI DSS assessment is to have very little in scope. We design payment paths so card data never touches your systems, which reduces both the audit burden and the consequences of getting something else wrong.
- Tokenisation and hosted fields to minimise scope
- Scope boundary documented and tested, not assumed
- Idempotent payment handling so retries never double-charge
- Reconciliation against the processor run continuously
Inventory truth
One stock number across every channel
Overselling is a customer-service problem created by an architecture problem. Channels reading separate stock views will diverge under load, and the divergence is always worst at peak.
- Single source of stock truth with reservations
- Event-driven propagation with ordering guarantees
- Oversell tolerance made an explicit business decision
- Reconciliation between channel and warehouse automated
Platform options
Four commerce architectures, honestly compared
Composable is fashionable and is not always right. The deciding factor is usually how much engineering capacity you can sustain.
| Approach | Time to launch | Ongoing engineering need | Best when |
|---|---|---|---|
| SaaS platform | Weeks | Low | Standard catalogue and checkout, small team |
| SaaS plus extensionsMost common | One quarter | Moderate | Differentiation sits in a few journeys |
| Composable / headless | Two to three quarters | High and permanent | Commerce is the business, not a channel |
| Custom build | Three quarters or more | Very high | A genuinely unusual model no platform fits |
SaaS platform
- Time to launch
- Weeks
- Ongoing engineering need
- Low
- Best when
- Standard catalogue and checkout, small team
SaaS plus extensions
Most common- Time to launch
- One quarter
- Ongoing engineering need
- Moderate
- Best when
- Differentiation sits in a few journeys
Composable / headless
- Time to launch
- Two to three quarters
- Ongoing engineering need
- High and permanent
- Best when
- Commerce is the business, not a channel
Custom build
- Time to launch
- Three quarters or more
- Ongoing engineering need
- Very high
- Best when
- A genuinely unusual model no platform fits
Questions
What retail clients ask first
Peak readiness, and whether re-platforming is really necessary.
Peak and platform
How close to peak can we safely make changes?
We recommend a code freeze on the checkout path four to six weeks out, with infrastructure and configuration changes still permitted under a tighter approval. The freeze is less about the risk of any single change and more about preserving a known-good state you can reason about at three in the morning.
Do we need to re-platform?
Frequently not. A load and architecture review usually finds that the constraint is a small number of query patterns, a caching strategy, or one synchronous call in the checkout path. Re-platforming is the right answer when the platform blocks the business model, not when it is slow.
Can you load-test against production data volumes?
Yes, using a production-shaped synthetic catalogue and traffic patterns replayed from your own telemetry rather than a generic script. Testing with unrealistic data is how platforms pass a load test in October and fall over in November.
Working in a different sector?
The engineering practice is the same across every vertical we serve. Browse the full list, or tell us what you are dealing with.
All industriesGet ready before peak, not during it.
A load and architecture review that tells you where your platform breaks, at what volume, and what it costs to fix.