Know what sells.Know what's on the shelf.Know what makes money.Then act on it.
Data Semantics gives retailers one view of what sells, what’s on the shelf and what makes money, across stores and online, and AI agents that act on it: flagging stock and margin risks, answering customers and processing supplier invoices, on the systems you already run.
For grocery, pharmacy, fashion, electronics, luxury and lifestyle retailers, and the multi-brand groups behind them.
Data Semantics provides analytics, automation and AI agents for grocery, pharmacy, fashion, luxury and multi-brand retailers. We connect POS, e-commerce, merchandising, inventory, supplier and finance data into one view of what sells, what is on the shelf and what makes money. AI agents then replenish stock, answer customer queries and post supplier invoices into the ERP, with people approving what matters. Start with either analytics or automation.
Buyers, merchandisers, supply chain, stores, e-commerce and finance each run on different systems. Every gap between them is where margin, stock and customers leak.
Four operating models, one foundation: connect the data, model the business, then automate the decisions that matter. Selling to businesses rather than shoppers? See trading and distribution.
Supermarkets, hypermarkets and convenience chains running on thin margins, high SKU counts and fresh stock.
Pharmacy chains balancing regulated stock and retail ranges across many stores.
Fashion, jewellery, beauty and lifestyle retailers where full-price sell-through and high-value stock decide the year.
Groups running many brands, formats and countries across stores, e-commerce and marketplaces.
We combine industry understanding with the technical know-how to build what you need, from analytics and automation to AI agents that act. These are the most common starting points, and the last card in each row covers everything else.
Merchandising and margin intelligence connects sales, purchase cost, rebates, promotions, markdowns and returns to show what each category, SKU and store really earns.
Inventory and replenishment intelligence combines sales velocity, stock positions, lead times and promotions to keep shelves full with less stock in the back.
Store and channel intelligence brings stores, e-commerce and marketplaces into one view of sales, traffic, conversion and productivity.
Customer and loyalty intelligence connects loyalty, transactions and service history to show who your customers are, what they buy and who is drifting away.
Need something else analyzed or automated? Talk to an expert →
AP and procurement automation captures, matches, approves and posts supplier and store-delivery invoices, credit notes and statements into your ERP, with a full audit trail.
Customer service automation answers order, delivery, return and product questions across chat, voice and messaging, and hands complex cases to your team with full context.
Tell us where your teams lose the most time today. We’ll show you what we would fix first, on the systems you already run.
A retail ontology is a shared model of your business: the SKUs, stores, suppliers, promotions, customers and transactions, how they relate, and which system each lives in. Your POS knows the sale, your merchandising system knows the promotion and your ERP knows the supplier invoice. The ontology knows they're the same SKU at the same store.
Two workflows retail teams often start with.
An empty shelf loses the sale today and the customer tomorrow.
Price, quantity and rebate mismatches caught before you pay, across every store and entity.
We don’t borrow results from other industries. Every retail engagement starts by measuring today’s baseline, and success is proven against it. These are the measures we track most often.
Supplier and store-delivery invoices captured, matched to purchase orders and receipts, and posted automatically, with exceptions routed to the right team.
Sales, stock and finance data from multiple ERPs and POS systems brought together for brand, country and group reporting.
Each is a fixed-scope engagement on your own data, measured against today’s baseline.
Sales, stock, margin and promotions by store, channel and SKU in one governed view.
Business Intelligence & Analytics →AutomationSupplier invoices captured, matched to orders and receipts and posted to the ERP, with exceptions to your team.
Serina →AutomationAI agents answer order, return and loyalty queries by chat, email and voice, and hand complex cases to your team.
Zebo →Every engagement follows the same path: measure today’s baseline, connect and model the data, prove it on a real workflow, then scale.
About retail AI
A retail ontology is a shared model of your business: SKUs, stores, suppliers, promotions, customers and transactions, and how they relate, mapped to the systems where each lives. It gives every report and every AI agent the same definitions and full context, which is what makes their answers and actions trustworthy.
Analytics
Inventory and replenishment intelligence combines sales velocity, stock positions, supplier lead times and promotions to show where shelves are about to run empty, where stock is excess and what to do about it, from transfers and reorders to markdowns.
Merchandising and margin intelligence brings sales, purchase cost, supplier rebates, promotions, markdowns and returns into one view, so you can see what each category, SKU and store really earns, not just what it sells.
Yes. Data Semantics brings store, e-commerce and marketplace sales, stock and orders into one model, so availability, margin and customer insight are measured the same way across every channel. Zebo's AI Conversations handle e-commerce customer service.
Systems and data
LS Retail, Oracle Retail, SAP, Oracle Fusion and EBS, Microsoft Dynamics 365 and AX and Salesforce are the most common, plus POS, warehouse, e-commerce, loyalty and banking systems. If your data is in a system, we can usually connect it.
No. Data Semantics builds on what you run today, including Microsoft Fabric, Databricks or Snowflake if you already use one. Where item, supplier or customer data needs cleaning or moving, DataPrep 360 handles it as part of the work.
Our solutions run on Microsoft Azure and can be deployed in your own tenant and preferred region, so your data stays under your control.
Getting started
Yes, and we recommend it. Common starting points are supplier invoice automation, store availability, margin and promotion analysis, and e-commerce customer service. Each new use case reuses the same ontology, so it goes live faster.
AI agents flag stock and margin risks, match invoices, answer customers and recommend next steps within guardrails you set. Business-critical decisions such as transfers, price changes and markdowns stay with accountable people, and every action is logged.
Against a baseline measured with you at the start, such as availability, sell-through, days of inventory, gross margin, shrink, cost per invoice or service resolution time.
Availability, margin, promotions or supplier invoices. We'll show you what it looks like automated, on your own data.
