We don't work across every industry. We go deep where we've built enough pattern recognition to know what works, what fails, and what the real data challenges look like in production.
The e-commerce AI problems we solve are almost always data problems wearing an ML costume. Identity fragmentation across channels, catalog inconsistency, missing behavioral signal. We fix the foundation before we build the model.
Two-stage retrieval-ranking architecture. User identity unification across web, app, email, and loyalty. Real-time session context. A/B testing framework built in.
Embedding-based search that understands intent, synonyms, and cross-category queries. Catalog enrichment to ensure every product is findable. Query understanding layer for long-tail searches.
Hierarchical forecasting across SKU, category, location. Promotional lift modeling. Stockout prediction with reorder triggers. Seasonal pattern decomposition.
Competitive price monitoring with response policy. Elasticity modeling by segment and category. Margin-aware pricing that balances conversion with profitability.
2 hours, no cost. We'll tell you what's possible and what isn't.
We deliberately don't serve every industry. Deep pattern recognition in a domain takes years to build. If your problem falls outside the domains we specialise in, we'll tell you, and if we know someone better suited, we'll say that too.