E-commerce AI that moves the metrics you actually care about.
3×
average revenue uplift from personalisation
< 200ms
visual search latency target
88%
demand forecast accuracy at 14-day horizon
AI features that drive revenue
Personalisation Engine
Real-time personalisation of homepage, category pages, and search results based on user behaviour, session context, and similar user patterns. Drives AOV and session depth.
Visual Search
Search by image across your entire catalogue. Users upload or camera-capture a product; results ranked by visual similarity. Critical for fashion, home, and beauty verticals.
Recommendation Engine
You-may-also-like, frequently-bought-together, and post-purchase recommendations - collaborative filtering combined with content-based signals for cold-start handling.
Demand Forecasting
SKU-level demand forecasting with 88% accuracy at 14-day horizon. Inputs: historical sales, price elasticity, seasonality, promotions, and external signals (weather, events).
Review Analysis & Insights
Sentiment analysis, topic extraction, and product insight generation from customer reviews at scale. Surfaces product issues, feature requests, and competitive mentions automatically.
The personalisation stack
User Embeddings
Dense vector representations of user preferences, updated in real-time from session events. Captures intent, style preference, and price sensitivity.
Item Embeddings
Product vectors trained on visual features, textual attributes, and co-purchase signals. Enables semantic product similarity across catalogue.
Ranking Model
Learns-to-rank model combining user-item relevance score with business rules (margin, stock, promotions). Outputs final ranked list per user.
A/B Testing Layer
Every personalisation change runs through controlled experiments. Statistical significance gates, guardrail metrics on revenue per session, and automatic rollback.
Visual search architecture
How we go from image input to ranked results in under 200ms - at catalogue scale.
Recommendation systems
Collaborative Filtering
Use when
Users with sufficient purchase history
Leverages collective wisdom - users who bought X also bought Y. Strong for popular SKUs, known user preferences.
Content-Based Filtering
Use when
New users, new products (cold start)
Relies on item attributes (category, colour, material) rather than interaction history. Works from day one.
Hybrid
Use when
Production systems at scale
Combines both signals with a learned weighting model. Better accuracy than either approach alone. Our default recommendation.
Forecasting for inventory
Inputs
Historical sales (24+ months)
Price & promotion history
Seasonality indices
Weather signals
Event calendar
Competitor pricing (where available)
Outputs
14-day SKU-level forecast
Confidence intervals (P10/P50/P90)
Reorder point recommendations
Overstock risk flags
Markdown trigger alerts
"Visual search across 4M SKUs at 180ms - 3× add-to-cart rate for a fashion retailer."
Fine-tuned vision model on 12M fashion product images. HNSW vector index across full 4M SKU catalogue. Real-time re-ranking with stock and margin signals. Deployed to iOS and Android in 8 weeks.
How personalisation fires for every shopper in under 100ms.
Session start
User arrives. Anonymous or recognised. Session context initialised: device, referrer, time.
Event stream
Click, scroll, add-to-cart events piped in real-time to user embedding update layer.
Embedding match
User vector matched against item embeddings. Top-N candidates retrieved from vector index.
Ranking pass
Business rules applied: margin, stock, promotions. Final ranked list generated per user.
Response served
Personalised page rendered. Results A/B tested continuously against control cohort.
Generic storefront vs. AI-powered retail.
The metrics that move when AI is built and deployed correctly.
Based on client deployments across fashion, home, beauty, and marketplace verticals.
3×
Average Order Value uplift
From personalisation engine - returning users shown curated sets based on embedding similarity to past purchases.
+47%
Session depth increase
Users who engage with AI search browse 47% more pages per session vs. keyword search users.
88%
Demand forecast accuracy
At 14-day SKU-level horizon across seasonal and promotional periods. Reduces markdown exposure by 31%.
3×
Add-to-cart rate on visual search
Fashion retailer: users who searched by image converted at 3× the rate of text search users.
−19%
Cart abandonment reduction
Post-add recommendation engine surfaces urgency signals and social proof to users with items in cart.
< 200ms
Visual search latency at 4M SKU scale
HNSW vector index over full catalogue. iOS and Android apps. P99 latency across peak traffic periods.