Multicloud AI & ERP / Cloud AI Applications & Industry AI Solutions
Why it matters
Margins are thin and customers switch quickly. Better search lifts conversion, personalization lifts basket size and accurate demand forecasts cut both stockouts and markdowns.
How it works
AI across the shopper journey
AI search understands intent, not just keywords. Recommendations personalize every page. A shopping assistant answers questions and compares products. Behind the scenes, demand forecasts put stock where shoppers are.
AWS, Azure and Google Cloud compared
| AWS | Azure | Google Cloud | |
|---|---|---|---|
| Product search | Amazon OpenSearch | Azure AI Search | Vertex AI Search for commerce |
| Recommendations | Amazon Personalize | Azure ML on Fabric data | Recommendations for commerce |
| Shopping assistant | Bedrock agents | Foundry agents | Conversational commerce with Gemini |
| Demand forecasting | SageMaker, Chronos | Fabric and Azure ML forecasting | BigQuery ML, TimesFM |
| Visual search | Amazon Rekognition | Azure AI Vision | Vision AI |
What makes each cloud different
AWS
AWS offers Amazon Personalize for recommendations and re-ranking, OpenSearch for semantic product search and Bedrock for shopping assistants, built on the same retail heritage as Amazon.com.
Azure
Azure combines AI Search with semantic ranking, Fabric for unified retail data and Foundry agents, with Microsoft Cloud for Retail providing data models and solutions.
Google Cloud
Vertex AI Search for commerce brings Google-quality product search and recommendations that learn from every click, and BigQuery ML forecasts demand at item and store level.
Design decisions that matter
- 01Fix search first
Lost searches are lost sales.
- 02Real-time behavioral signals
Personalize on today's intent.
- 03Forecast at item and store level
Averages hide stockouts.
- 04Test everything
A/B tests tied to revenue.
Common mistakes we see
- Keyword-only search. Zero-result searches send shoppers away.
- Recommending unavailable items. Wasted slots and frustration.
- Ignoring promotions in forecasts. The biggest demand driver missed.
A practical 30-day plan
- Week 1
Measure zero-result searches and conversion.
- Week 2
Launch AI search on part of the catalog.
- Week 3
Add recommendations with stock-aware rules.
- Week 4
Pilot item-level demand forecasting.
How NTS helps
NTS builds retail AI on AWS, Azure and Google Cloud, covering AI search, personalization, shopping assistants and demand forecasting, all measured by conversion and inventory outcomes.
What you get
- Personalization and recommendation engines
- Demand forecasting for inventory
- AI shopping assistants
- Pricing and promotion analytics
Typical outcomes
- Higher conversion and basket size
- Less stockout and overstock
- Better customer experiences
How we work with you
- 1Assess
A focused review of your retail AI across AWS, Azure and Google Cloud. We look for the gaps we find most often: keyword-only search, recommending unavailable items and ignoring promotions in forecasts.
- 2Design
A target architecture and roadmap built on the decisions that matter most: fix search first, real-time behavioral signals, forecast at item and store level and test everything.
- 3Build
Hands-on delivery alongside your team. We measure zero-result searches and conversion, launch AI search on part of the catalog, add recommendations with stock-aware rules, and finally pilot item-level demand forecasting.
- 4Run and improve
We retrain models with fresh sales data and tune for every season.
Why teams choose NTS for retail AI
- Revenue-focused AI
We measure success in sales, margin and loyalty.
- Omnichannel ready
Our designs work across stores, web and apps.
- Seasonal peaks handled
Systems scale for the busiest days.
Book a free 30-minute review
Talk to an NTS architect about your retail AI on AWS, Azure and Google Cloud.