Global Gate Marketplace
An AI-driven global marketplace where discovery, pricing and demand are governed by machine intelligence.
The brief
The client operated across multiple regions but ran each storefront as an isolated property — separate catalogues, separate pricing rules, and no shared view of demand. Shoppers saw generic listings, merchandising was manual, and pricing decisions lagged the market by days.
They needed a single marketplace that could merchandise itself: surface the right product to the right buyer, price dynamically against live demand, and forecast what each region would want next — without growing the operations team in proportion to the catalogue.
- Unify isolated regional storefronts into one marketplace with a single catalogue and a shared view of demand.
- Personalise discovery for every shopper instead of a fixed, manually merchandised catalogue.
- Price dynamically against live demand, competition and inventory — within margins the team controls.
- Forecast demand per region and category to drive replenishment and clear slow stock earlier.
- Hold sub-second search and discovery through seasonal peak traffic.
- Scale the catalogue without scaling the operations team in proportion.
Our AI-native approach
We treated discovery, pricing and demand as one connected intelligence problem rather than three features. A unified product graph became the backbone; on top of it we layered a recommendation engine, a sentiment and review-mining pipeline, and a demand-forecasting model that all read from the same event stream.
Delivery followed our AI-driven agenda end to end — from mining the existing transaction history during discovery, to AI-assisted build, to automated evaluation of every model before it touched live traffic.
What we built
Personalised discovery
A recommender blends collaborative filtering with content embeddings so each shopper sees a storefront ranked to their intent, not a fixed catalogue.
Dynamic pricing engine
Prices adjust against live demand, competitor signals and inventory depth within guardrails the merchandising team sets.
Sentiment & review mining
NLP distils thousands of reviews into product-level quality signals that feed both ranking and buyer trust badges.
Demand forecasting
Per-region, per-category forecasts drive replenishment and surface stock that is about to run cold.
Unified product graph
One canonical catalogue resolves duplicates and variants across regions, so every model reads clean, shared data.
Merchandiser console
A React console lets the team steer the AI — pin products, set price floors, and review model decisions with full transparency.
How we built it
We opened with a focused discovery phase, mining the client's historical transactions to size the opportunity and confirm that personalisation and dynamic pricing would genuinely move revenue before committing to a full build. The very first thing we shipped was a unified product graph, because every model downstream depended on clean, de-duplicated, shared data.
From there a senior-led pod built in fortnightly increments, with AI pair-programming accelerating delivery. The recommender, pricing optimiser and forecasting models were rolled out one at a time and measured independently, each placed behind an evaluation gate — offline metrics plus a shadow period against live traffic — so nothing reached shoppers unproven.
How it works
Ingest
Orders, clicks, reviews and inventory stream into a unified event log.
Enrich
Products are embedded and resolved against the canonical graph.
Decide
Recommendation, pricing and forecast models score in real time.
Serve
Ranked, priced storefronts render through a cached API layer.
Learn
Outcomes feed back nightly; models are re-evaluated before promotion.
The recommendation layer combines implicit behaviour signals with text and image embeddings, so cold-start products still find an audience. Pricing runs as a constrained optimiser — the model proposes, the guardrails dispose — which keeps margins safe while staying responsive.
Every model ships behind an evaluation gate: offline metrics plus a shadow period against live traffic, so nothing reaches shoppers unproven.
The impact
Personalised storefronts measurably outperformed the previous fixed merchandising across every region.
Search and discovery held sub-second response through seasonal peak traffic.
Dynamic pricing recovered margin on fast movers while clearing slow stock earlier.
The operations team scaled the catalogue without scaling headcount.
Technology stack
Building something similar?
If this maps to a problem you're facing, tell us what you're building. We'll show you how we'd engineer it — and come back within one business day.