E-Commerce Solutions
Storefronts and checkout flows built around conversion, with AI recommendations and search that use your catalogue and your customers rather than a generic model.
- Shopify & headless
- Checkout conversion
- AI recommendations
- Semantic search
Most lost revenue is lost at checkout, not at the top of the funnel
Teams spend heavily on traffic and then hand it to a checkout that takes six seconds to respond on mobile. Fixing the last three steps is usually cheaper than buying more visitors, and the effect compounds against every campaign that follows.
Capabilities in this practice
Each of these ships as a working system integrated with what you already run — not a slide deck or a proof of concept that stalls at the pilot.
Storefront Builds
Shopify, WooCommerce or headless front ends, chosen against your catalogue size and merchandising needs rather than habit.
Checkout Optimisation
Fewer steps, faster responses, honest error handling and payment methods your customers actually use — measured against completion rate.
AI Recommendations
Recommendations trained on your catalogue and behaviour, with cold-start handling so new products and new visitors are not left out.
Semantic Product Search
Search that understands intent and attributes rather than matching keywords, so "warm jacket for hiking" returns the right products.
Inventory & Operations
Stock synchronisation, demand signals and fulfilment integrations connecting the storefront to how the business actually runs.
Analytics & Experimentation
Event tracking and A/B infrastructure so merchandising decisions are settled by data instead of seniority.
A sequence built to de-risk, not to impress
We measure before we optimise and ship in slices, so you can stop, redirect or scale at any step with evidence rather than instinct.
- 1
Funnel audit
Instrument the journey and find where revenue is actually leaking.
- 2
Fix the checkout
Address the highest-drop step first; it is nearly always the fastest return.
- 3
Platform decision
Stay, extend or go headless — decided on catalogue and roadmap, not on fashion.
- 4
Build
Storefront, search and recommendations shipped behind flags where sensible.
- 5
Measure
Every change judged on conversion and revenue per session, not on impressions.
- 6
Iterate
Continuous experimentation on merchandising, search relevance and pricing presentation.
Commerce work
We build our own products on this stack. When we recommend an approach, it is one we already run in production and pay the bills for.
The things clients ask before signing
Shopify for most catalogues — you get payments, tax, fraud and hosting solved, and that is a lot of undifferentiated work to take back in-house. Headless earns its cost when merchandising needs go beyond what the theme layer allows or when commerce is one surface inside a larger product.
Small catalogues do better with attribute and rules-based recommendations than with collaborative filtering, which needs interaction volume to say anything useful. We size the approach to your data instead of applying the same model everywhere.
Performance and step-reduction changes usually show in completion rate within a couple of weeks, because they affect every session immediately. Recommendation and search work takes longer to read, since it depends on accumulating enough interactions to measure honestly.
Ready to put this into production?
Tell us the problem you are trying to solve. We will tell you honestly whether AI is the right tool for it, and what it would take to ship.