🛒 Retail AI - Commerce Intelligence Systems

Retail AI that the merchandiser
and the auditor both trust

AI systems for retail and e-commerce - demand forecasting, dynamic pricing, personalization, inventory, fraud. Every recommendation explainable, every model documented, every event captured. Built for PCI-DSS, GDPR and CCPA.

Consultation call
⚠️ Regulated domain

Retail AI is not a black box

Every decision needs an explanation - for the regulator, for the user and for the internal auditor.

100%
Explainable Decisions
PCI-DSS
card-data scope
GDPR + CCPA
compliance
Audit-aware
design
⚙️ Retail AI solutions

End-to-end AI for the sector

📈

Demand Forecasting

SKU-level demand forecasting for procurement and merchandising.

  • Hierarchical forecasting (store / region / national)
  • MAPE / WAPE reported per category
  • External signal fusion (weather, events, promotions)
  • Cold-start for new SKUs
💰

Dynamic Pricing

Price elasticity modelling with merchandiser-in-the-loop.

  • Cross-elasticity within category
  • Promo lift attribution
  • Guardrails: minimum margin, MAP compliance
  • A/B-tested before rollout
🎯

Personalization and Recommendations

Per-shopper signal, not just segment.

  • Cold-start via item embedding
  • Session-aware ranking
  • Diversity + serendipity controls
  • Multi-objective (CTR, AOV, margin)
📦

Inventory Optimization

Safety stock and replenishment with realistic lead-time distributions.

  • Multi-echelon optimization
  • Lead-time variability modelling
  • Stockout vs holding-cost trade-off explicit
  • Integration with WMS / ERP
🛡️

Fraud Prevention

Account-takeover, promo-abuse, refund-fraud detection.

  • Device fingerprinting + behavioral biometrics
  • Velocity checks (per IP, per card, per device)
  • Graph analysis for rings
  • False-positive cost modelling
🔐

Model Governance

Documentation, monitoring and rollback of every production model.

  • Model registry with lineage
  • Drift detection on key features
  • Champion / Challenger flow
  • Reproducible feature engineering
📋 Regulation

Built for every regulator in the room

🇺🇸

US: PCI-DSS, CCPA, CPRA

PCI-DSS v4.0 for card data, CCPA / CPRA for California consumer data, FTC fairness in algorithmic pricing, state-level price-gouging regulations.

🇪🇺

EU: GDPR, EU AI Act, DSA

GDPR for shopper data, EU AI Act Article 6 / 9 / 10 (recommender + price discrimination risk classification), Digital Services Act for marketplace platforms, P2B regulation.

🌍

PCI-DSS scope reduction and segregation of duties

Tokenization, secret-management, segregation of duties between commerce + analytics workloads, full audit trail - designed to reduce PCI scope and pass external audit.

📊 Industry benchmarks

What to expect from Retail AI that works in production

Most models fail not in the demo - but in production: drift, audit, integration. Here is what the public industry research reports and where our architectural contribution comes in.

📈

Demand forecasting accuracy

What the industry reports: Top-quartile retailers report 15-25% MAPE reduction at SKU-week level vs naive baseline when combining hierarchical reconciliation + external signals (Gartner retail forecasting 2024).

Where we come in: Hierarchical forecasting (store / region / national), external signal fusion (weather, events, promotions), MAPE and WAPE reported per category - not aggregate.

  • MAPE / WAPE reported per category, not aggregate
  • External signal fusion (weather, events, promotions)
  • Cold-start for new SKUs without bias to top sellers
  • Reconciled forecasts across the hierarchy
💰

Dynamic pricing lift

What the industry reports: Retailers running price elasticity modelling with merchandiser-in-the-loop see 2-5% margin lift in tested categories (McKinsey retail 2024).

Where we come in: Cross-elasticity within category, promo lift attribution, guardrails (minimum margin, MAP compliance), A/B-tested before rollout - merchandiser stays in the loop on every category.

  • Cross-elasticity captured (not single-SKU elasticity)
  • Promo lift attribution separated from baseline
  • Guardrails on minimum margin and MAP
  • A/B tested before category-wide rollout
🎯

Recommender CTR

What the industry reports: Per-shopper-signal recommenders report 18-30% CTR uplift over segment recommenders, with biggest gains on cold-start sessions (Bain digital retail 2024).

Where we come in: Cold-start via item embedding, session-aware ranking, diversity / serendipity controls, multi-objective optimization (CTR, AOV, margin) - not just last-click optimization.

  • Cold-start via item embedding (handles new shoppers)
  • Multi-objective (CTR, AOV, margin) not just last-click
  • Diversity and serendipity controls
  • Session-aware ranking

* SLAtech, since 2004. We have worked with retailers and e-commerce platforms in 14 countries. Commercial examples available on a consultation call.

Ready to build Retail AI that the merchandiser actually trusts?

30-minute consultation call - we map the merchandising use cases, propose an architecture, and quantify ROI.