AI that sells, measured like anything else.
For catalogues too large to merchandise by hand. Recommendations, search and assistants that move revenue.
- Timeline
- 6–10 wks
- Team
- 2–4
- From
- Scoped after a call
- Platforms
- Shopify, custom
ProblemSound familiar?
- Search that returns nothing
- Generic “you may also like”
- A chatbot nobody uses
We engineerWhat we engineer
Recommendation and search systems trained on your own catalogue and behaviour, shipped behind experiments so the uplift is proven.
ArchitectureTypical architecture
- Catalogue + events
- Embedding / ranking service
- Storefront widgets
- Experiment layer
CapabilitiesCapabilities
- Semantic search
- Recommendation engines
- Shopping assistants
- Uplift measurement
DeliverablesDeliverables
- Model and service
- Storefront integration
- A/B readout
ProcessProcess
Discover
We learn the business before the brief: the metric that matters, the constraints, the people involved.
- Kick-off workshop with your decision-makers
- Access to analytics, code and tools
- Agree the one metric this work must move
- Kick-off notes and a shared goal
- Access checklist
- Named pod lead
We learn the business before the brief: the metric that matters, the constraints, the people involved.
What happens- Kick-off workshop with your decision-makers
- Access to analytics, code and tools
- Agree the one metric this work must move
- Kick-off notes and a shared goal
- Access checklist
- Named pod lead
StackStack
WorkWork
Often. We regularly take the engineering layer while a brand or performance agency keeps creative and media. We agree on ownership in week one so nothing falls between teams.
Yes. Most commerce work can start as a productized sprint with a fixed price and fixed deliverables — see Programs. Larger rebuilds are scoped after an audit.
Programs →Always a duplicate theme or staging environment first. Nothing reaches production without a review and a rollback plan.
Start with ai commerce.