Case Studies

Walmart built a generative AI-powered search engine using Azure OpenAI Service and proprietary retail models, enabling shoppers to submit natural-language, occasion-based queries and receive curated product bundles. The company reported 22% global ecommerce growth in the quarter when the capability went live.

US beauty retail leader Ulta Beauty built Quazi, a proprietary AI personalization platform, to recommend individualized product combinations across marketing, social, and sales channels — leveraging insights from its 46M+ member loyalty base.

Amazon's Catalog team applies generative AI to enrich product data at scale and uses causal random forests and Bayesian structural time series to rigorously measure whether enrichments improve customer decisions and conversion.

Walmart deployed large language models across its product catalog to create or improve more than 850 million pieces of data, dramatically improving search accuracy, store associate efficiency, and inventory operations.

Target built the Inventory Ledger — a machine learning ensemble that infers inventory inaccuracies from underlying data patterns and automatically corrects them — processing up to 360,000 inventory transactions per second across nearly 2,000 U.S. stores to ensure products are available and replenishment is triggered.

Walmart's centralized AI/ML inventory platform analyzes demand at the individual item-store-day level, enabling proactive replenishment, geographic redistribution of inventory, and anomaly 'forgetting' to avoid over-correcting on one-time events.

UK media ecommerce operator Monwell deployed Athos Commerce's intelligent search and personalized recommendations across four brand stores, driving 6× higher conversion from search users and 7× higher revenue per visit — with 34% of total revenue attributed to search.

Walmart's automated contract negotiation system using Pactum AI secured agreements with 68% of suppliers and generated ~1.5% cost savings, while supply chain automation improved average unit costs by 20%.

Working with OutSystems and local partner Synobsys, DMG replaced a paper-heavy home-measurement process with a mobile app built in weeks, saving more than 1.6 FTEs of manual work annually.

After shifting to digital-first retail in 2018, Marks & Spencer deployed New Relic to gain end-to-end observability, reducing MTTR by one-third, cutting incident team sizes from 80 people to a targeted notification, and achieving full service visibility across thousands of digital touchpoints.

By combining session recordings, heatmaps, and 30 targeted A/B tests over 17 months, the luxury lighting retailer turned a chronic conversion problem into an 18× ROI.