Audit & signal mapping
Mapped browsing, cart, and inventory data to find where the static best-seller list was leaving revenue on the table.
RETAIL · CASE STUDY
ShopWave lifted revenue per visit +29%
A real-time recommendation and demand-forecasting engine that lifted revenue per visit by 29%.
- 14 weeks to production
- Session-aware, real-time
- Inventory-linked recs
ShopWave Live+29%revenue / visit
Capabilities
Session-aware recs
Demand-linked inventory
Real-time updates
Storefront-native
The live recommendation feed
Every session tracked as it browses — recommendations update the moment intent shifts.
LIVE SESSIONS — RECOMMENDATION FEEDAUTO-REFRESH
SES-90213Browsing running shoes3 product views · high intent signalsHIGH INTENTlive
SES-90208Browsing winter jacketsComparing 2 similar itemsCOMPARING2 min
SES-90195Cart: 2 items, $148Checkout in progressIN CART6 min
SES-90180Browsing homepageFirst visit, no signals yetBROWSING11 min
Recommendations update live as browsing signals change — always backed by inventory that can actually fulfill them.
Why ShopWave called us
Product recommendations were static and rules-based, the same handful of "popular items" shown to every visitor regardless of browsing behavior, while inventory planning ran on historical averages that missed real-time demand shifts.
Replace static best-seller recommendations with session-aware picks backed by real inventory.
14 weeks to productionTimeline
AI engineers + e-commerce leadTeam
From audit to production
Why 14 weeks was realistic — the scope was one recommendation engine and one forecasting layer, not a storefront rebuild. Here's the breakdown:
Recsys & forecasting build
Built the session-aware recommendation model and the demand-forecasting layer that feeds the same signals into inventory planning.
Storefront integration
Wired both directly into the existing storefront — no rebuild of the shopping experience itself.
Live rollout & tuning
Rolled out across the full catalog, then tuned on real session and conversion data during the first weeks live.
What we delivered
The stack behind ShopWave's real-time recommendation and forecasting layer.
Session-aware ranking + demand forecasting
A real-time ranking model re-scores the catalog against each session's live browsing signals, while a separate forecasting model feeds those same signals into inventory planning — so what gets recommended never outruns what can actually ship.
Python
TensorFlow
Kafka
FastAPI
PostgreSQL
Redis
Docker
Storefront integration
Plus real-time inventory sync feeding the same signals back into merchandising — the connective work most off-the-shelf recsys tools skip.
The challenge
Product recommendations were static and rules-based, the same handful of "popular items" shown to every visitor regardless of browsing behavior, while inventory planning ran on historical averages that missed real-time demand shifts.
What we built
We shipped a real-time recommendation engine that adapts to each session's actual browsing signals, paired with a demand-forecasting layer that feeds those same signals back into inventory planning — so what's recommended and what's in stock move together.
Core capabilities
REC
Session-aware recommendations
Picks adapt to each visitor's actual browsing behavior, not a fixed best-seller list.
INV
Demand-linked inventory
The same signals driving recommendations feed directly into inventory planning.
RT
Real-time updates
Recommendations and stock forecasts update live, not on a batch schedule.
SF
Storefront-native
Drops into the existing storefront — no rebuild of the shopping experience.
Inside ShopWave
The recommendation engine merchandising uses every day, from sign-in to inventory log.
The storefront — what the shopper actually sees
Merchandising view — recommended live, reasoned, and in stock
Dashboard — revenue per visit & conversion
Login — storefront admin access
Detail — why one product was recommended
Inventory log — stock feeding recommendations
The outcome
Revenue per visit rose 29% as recommendations shifted from generic best-sellers to session-relevant picks backed by inventory that could actually fulfill them.
+29%revenue / visit
14weeks to production
3AI systems shipped
What the merchandising team is saying
Straight from the team running the storefront every day.
“We were recommending the same best-sellers to everyone regardless of what they'd actually clicked on and inventory planning ran on last quarter's averages. Bounce tied recommendations to real session behavior and fed that straight into demand forecasting so what we show someone is also what we can actually ship them. That combination is what moved revenue per visit not a fancier algorithm.”
Priya NairE-commerce Director, RetailFrequently Asked Questions
Yes — the same signals driving recommendations feed directly into inventory planning, so what's suggested and what's fulfillable move together.
In real time, as a session's browsing behavior changes — not on a nightly batch job like the old best-seller list.
No — it drops into the existing storefront. The shopping experience didn't change, only what gets recommended.
14 weeks from kickoff to production, covering the recommendation engine, the demand-forecasting layer, and storefront integration.
Session-relevant picks backed by real inventory, replacing generic best-sellers shown to every visitor regardless of what they were actually browsing.
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