AI that moves product - and moves the metrics that matter.
We build AI systems for omnichannel retailers and distributors - from personalisation engines that increase basket size to demand forecasting that reduces overstock write-offs.
30%
Average reduction in overstock cost
2.4×
Conversion uplift from personalisation
88%
Demand forecast accuracy at 90-day horizon
40ms
Avg. visual search response time
What we build
Demand Forecasting
Time-series ML models that predict SKU demand 30–90 days out across stores, regions, and channels - reducing overstock and stockout costs by 20–40%.
Personalisation Engines
Real-time recommendation systems that personalise product listings, promotions, and email content for individual shoppers - increasing basket size and repeat purchase rate.
Visual Search & Discovery
Computer vision that lets customers find products by uploading a photo - or automatically generates tagged product images for search and filter functionality.
Inventory Optimisation
AI-driven replenishment models that balance holding cost against stockout risk across multi-location distribution networks, integrating with ERP and WMS systems.
Pricing Intelligence
Dynamic pricing models that respond to competitor pricing, demand signals, and margin targets - with guardrails to prevent race-to-the-bottom scenarios.
Loss Prevention
Computer vision and anomaly detection for in-store loss prevention - identifying suspicious behaviour patterns without requiring extensive manual review.
The transformation
Retail without AI vs. retail with AI
How we deliver
From discovery to production in 12 weeks
01
Data audit
Review existing POS, WMS, CRM, and ecommerce data sources. Identify gaps and quality issues.
02
Data pipeline
Build ingestion layer connecting all data sources. Unified customer and product graph established.
03
Model training
Demand forecasting and personalisation models trained on your historical data. Baseline metrics set.
04
Integration
Models integrated with ecommerce platform, ERP, and WMS. A/B test infrastructure deployed.
05
Live & measure
Go live with monitoring dashboards. KPIs tracked weekly. Model retrained on fresh data monthly.
Case study
Fashion retailer: personalisation at 42M sessions/month
A mid-market fashion retailer with 380 stores and an ecommerce channel serving 42M sessions per month needed to move beyond static "bestseller" recommendations. Their existing personalisation was rule-based and channel-blind.
We built a real-time recommendation engine using session-level collaborative filtering, purchase graph embeddings, and inventory-aware ranking - deployed across web, app, and email channels from a single model layer.
2.4×
Conversion uplift vs. rule-based baseline
+18%
Average basket value increase
40ms
P95 recommendation API latency at peak
12 wk
Time from data audit to production go-live
Common questions
What retail clients ask us first
Do you work with Shopify / Salesforce Commerce Cloud / Magento?
Yes. We integrate with all major ecommerce platforms via their APIs. For personalisation and recommendation systems, we typically build a middleware layer that works alongside your platform's native features rather than replacing them.
How much historical data do we need for demand forecasting to work?
Typically 2+ years of transaction history gives the strongest results - enough to capture seasonality and promotional patterns. We can begin with 12 months and use transfer learning from category-level patterns to compensate for sparse SKU history.
Can personalisation work for a retailer with a small SKU count?
Yes - personalisation is as much about the customer journey as the catalogue size. For smaller catalogues, we focus on sequencing, timing, and channel optimisation rather than purely catalogue ranking, and content-based features replace collaborative filtering signals.
How do you handle data privacy for personalisation in GDPR markets?
Personalisation data is processed on a lawful basis (legitimate interest or consent, depending on your setup). We implement consent management integration, data minimisation, pseudonymisation, and user-level deletion pipelines as standard in EU deployments.