AI Driven Digitalisation
Modernize omnichannel order management, loyalty, and store back office.
Legacy Modernization Bespoke DevelopmentRetail AI That Shows Up in Margin.
Retail demand forecasting, assortment and markdown optimization, real-time personalization, store-associate copilots, and returns triage, built on one customer 360 and measured per use case.
- 4-6ptlift in on-shelf availability
- 27%lower forecast error
- 60daysto measurable store-copilot lift after pilot
Challenges
Where Retail Margin Leaks
- 01
Weekly Decisions in a Daily Market
Assortment, pricing, and replenishment run on last week's data.
- 02
Customer Data in Silos
Store, web, app, and loyalty each see a different customer.
- 03
Markdowns and Returns Eat Profit
Late markdowns and unchecked returns quietly erase margin.
Value chain
Where AI Moves the Needle in Retail
AI in retail pays off fastest in demand forecasting, assortment and markdown decisions, and personalization, where small accuracy gains move margin across thousands of SKUs and stores. SMI TechSolutions maps use cases to each stage and measures lift per use case.
Stage 01
Stock what each region will actually buy.
- Retail demand forecasting
- Assortment planning by store cluster
- Replenishment analytics
Stage 02
Mark down earlier and smarter, not deeper.
- Markdown optimization
- Price elasticity analytics
- Sell-through tracking
Stage 03
One customer view across store, web, app, and loyalty.
- Omnichannel customer 360
- Real-time personalization
- Content generation pipelines
Stage 04
Give associates answers on the floor, on mobile.
- Store-associate copilot
- Store-ops cockpit
- Omnichannel order management
Stage 05
Route returns to the cheapest good outcome.
- Returns triage agent
- Return-reason analytics
- Fraud signals
Use-case spotlights
Three Places Retailers Start
Demand and Assortment
- Problem
- Stores run out of best sellers and over-stock the rest.
- We build
- Retail demand forecasting and assortment planning by store cluster and regional demand.
- KPI
- 4-6 point lift in on-shelf availability
Markdown Optimization
- Problem
- Markdowns come too late and cut too deep.
- We build
- Markdown recommendations from price elasticity and sell-through analytics.
- KPI
- Better gross margin on clearance
Real-Time Personalization
- Problem
- Every channel treats the customer as a stranger.
- We build
- Personalization from customer propensity and purchase history, with consent checked at every decision.
- KPI
- Higher conversion
Every model ships with an eval harness that measures lift against a control group.
Fits your stack
Built on the Retail Systems You Already Run
Your systems
- POS
- Ecommerce (Salesforce Commerce, Shopify, Magento)
- ERP and SAP CAR
- Order and warehouse management (Manhattan)
- Loyalty and CDP
SMI data layer
An omnichannel customer 360 on a governed lakehouse, with consent checked at retrieval and personalization time
Outcomes
- Merchandising models
- Personalization
- Store-associate copilot
- PCI DSS-aware delivery for payment data
- CCPA and state privacy laws built into the customer 360
- Personalization decisions logged for audit
Services that apply
How Our Services Apply to Retail
Data Engineering & BI
Customer 360, a retail lakehouse, and store-ops cockpits.
Data Analytics Power BIGen AI Services
Store copilots, returns triage agents, and content pipelines.
RAG AI-Centric BespokeFAQ
Frequently Asked Questions
How is AI used in retail?
AI in retail forecasts demand by store and SKU, sets prices and markdowns, personalizes offers, and gives store teams a copilot for stock, tasks, and policy questions. The common thread is one clean data layer across POS, ecommerce, and supply chain.
What is retail demand forecasting?
Retail demand forecasting predicts what each store and channel will sell, by SKU and by week, using sales history, promotions, seasonality, weather, and local events. Better forecasts mean fewer stockouts and less excess inventory.
How does markdown optimization work?
Models estimate price elasticity and sell-through for each item, then recommend when and how deep to discount so you clear stock while protecting margin. Merchants approve or adjust every recommendation.
How do you personalize without breaking privacy rules?
We build a consent-aware customer 360: only data a shopper agreed to share is used, opt-outs flow through to every model, and the design aligns with CCPA and PCI DSS requirements. Payment data never enters the personalization layer.
What is an AI retail analytics platform?
It is a governed data layer plus models and dashboards that answer merchandising, pricing, and customer questions in one place. We build it on your existing stack, such as Azure, Snowflake, or Databricks, rather than adding another silo.
How fast do store copilots show results?
Most pilots reach store teams within 60 days and are measured on task time and on-shelf availability.
Do you work with our commerce and merchandising systems?
Yes. We integrate with Salesforce Commerce Cloud, Shopify, Magento (Adobe Commerce), SAP CAR, Manhattan, and common POS and loyalty platforms.
How quickly can we see results?
Start with a 60-minute briefing. We then scope a 90-day pilot on one use case, such as forecasting for one category, with KPIs agreed up front.
Retail offer
Book a 60-Minute Retail Briefing.
Bring one margin problem, such as stockouts, markdowns, or returns. You leave with relevant case examples and an outline for a 90-day pilot.
Book a Briefing- Journey dating back to 2006
- US delivery leadership
- Aligns with CCPA and PCI DSS
- Works on your existing stack
