AI in ecommerce
AI that movesthe numbers a merchant watches
Search that understands intent, recommendations grounded in your own catalogue, and forecasting that tells a buyer what to order — the same forecasting stack we shipped into a trading ERP.
500+
Projects delivered
most under NDA
100+
Clients served
5 regions
5
Systems live
in production now
—
Clutch rating
awaiting verified score
What we build
The work itself,component by component
Semantic product search
Natural-language queries matched against your catalogue, including the attributes buyers actually type.
Recommendations
Behaviour and catalogue signals combined, with the cold-start case handled deliberately.
Demand forecasting
SKU-level projections with confidence bands, delivered into the purchasing screen.
Catalogue enrichment
Generated descriptions and attributes, schema-validated and queued for review.
Support assistants
Order status, returns, and policy questions answered from your own documentation.
Fraud and anomaly signals
Per-account baselines so an alert means unusual, not merely large.
Why it matters
What changesonce it is running
Three things a buyer of this work should be able to hold us to.
Fewer dead searches — Semantic matching finds the product when the shopper does not know your naming.
Better ordering — Forecasts arrive where the purchase decision is made.
Support that scales — Routine order questions resolve without a ticket.
Stack we use for this
- Python
- pgvector
- Next.js
- PostgreSQL
- Redis
- AWS
How it runs
Four stages,and you can leave after any of them
Scope
We agree what the system does and what shipping means, in writing.
Build
Short cycles in your repository, running before it is finished.
Ship
Deployed into your cloud account, reachable by a real user.
Stay
Maintenance and roadmap by the same engineers who built it.
Proof
The systemsthat back this page
FAQ
Questionswe get asked
Do you replace our storefront?
No. These sit alongside it and integrate through its APIs.
How much catalogue data is needed?
Enough to embed meaningfully — we check that in the assessment before quoting.
Will recommendations work on launch day?
Cold start is handled with catalogue similarity until behavioural data accumulates.
AI in ecommerce