Turn every transaction into a better next decision.
Every purchase, product, promotion, customer interaction and inventory movement creates another signal about what’s happening in your business.
Margins builds AI into retail operations to detect what matters, predict what may happen next and turn intelligence into better commercial and operational decisions.
From millions of signals to the decisions that matter.
Retail doesn’t have a data problem. It has an attention problem.
Thousands of products across hundreds of categories and locations generate millions of transactions. The business cannot investigate everything.
- 01Sales change.
- 02Demand moves.
- 03Stock disappears.
- 04Products underperform.
- 05Customer behaviour shifts.
- 06Promotions change purchasing patterns.
- 07Margins move.
- 08Something unusual happens in one location.
- 09Something else starts happening across twenty.
The opportunity for AI is not to create more information. It’s to determine what deserves human attention.
Yesterday’s dashboard tells you what happened. AI can help you understand what’s changing.
- What did we sell?
- Which store performed best?
- Which products declined?
- What was our margin?
- What changed unexpectedly?
- Where is demand moving?
- Which pattern deserves attention?
- What may happen next?
- What should somebody investigate?
- What action should we consider?
Transactions
Millions of signals
Reporting
What happened?
Detection
What’s changing?
Prediction
What may happen next?
Recommendation
What deserves attention?
Action
What should we do?
Move from looking at the business to continuously sensing the business.
Demand rarely changes everywhere at once.
A product accelerates in three stores. Another slows in one region. A category leaves its seasonal pattern. AI can analyze historical and current patterns across products, categories, stores, regions, channels, time periods, promotions, customers and inventory.
- Demand forecasting
- Anomaly detection
- Trend detection
- Replenishment signals
- Planning support
The earlier you understand that demand is changing, the more options the business has to respond.
Every empty shelf and every unsold product has a cost.
- Lost sales.
- Poor availability.
- Customer frustration.
- Emergency replenishment.
- Working capital tied up.
- Markdown pressure.
- Storage costs.
- Waste or obsolescence.
The objective isn’t less inventory. It’s better inventory.
Something changed. AI can tell you where to look.
Imagine a retailer with 20,000 SKUs across 80 locations. Nobody can manually evaluate every product-location combination every morning.
- A product suddenly stops selling at one store.
- A category declines despite stable overall traffic.
- A location’s returns become unusual.
- A normally stable SKU accelerates unexpectedly.
- One region behaves differently from comparable regions.
Don’t ask people to watch everything. Ask AI to tell them where to look.
- 20,000 products × 80 storesgrid
- 1,600,000product-location combinations
- AI detectioncontinuous
- 37unusual changes
- 6worth investigating
- Human attentionfocused
Every basket says something about customer behaviour.
Transaction history can reveal relationships between products that aren’t obvious when products are analyzed individually — supporting merchandising, assortment, promotions and commercial planning.
- 01Which products are purchased together?
- 02Which categories lead to another purchase?
- 03What products substitute for one another?
- 04What disappears when another product is unavailable?
- 05How do baskets change between locations?
- 06How do promotions affect the rest of the basket?
- 07Which products behave differently from comparable products?
A transaction tells you what sold. A pattern of transactions can tell you how customers behave.
Relevant beats generic.
For retailers with identifiable customer relationships, AI can combine purchasing behaviour, preferences, frequency and context to make interactions more relevant.
Good personalization isn’t showing customers more. It’s understanding what is relevant enough to show at all.
- What does this customer normally buy?
- What has changed?
- What might they need next?
- Which offer is actually relevant?
- When should we communicate?
- When should we leave them alone?
Sales increased. But did the promotion actually work?
- Incremental demand
- Cannibalization
- Margin
- Customer behaviour
- Post-promotion effect
- Did it create incremental demand?
- Did customers simply buy earlier?
- Did another product lose sales?
- Did margin improve?
- Did new customers buy?
- Did existing customers stock up?
- Did behaviour continue after the promotion?
- Was the effect different by location?
The important question isn’t whether sales moved. It’s what actually caused them to move.
Head office shouldn’t be the only place with intelligence.
Store managers and employees make hundreds of decisions every day. Instead of another dashboard, the most useful AI interface may be a single question:
What needs my attention today?
The best retail intelligence reaches the person who can still change the outcome.
- !Stock anomaly: SKU 4471 — investigate count
- ↘Category B below forecast since Tuesday
- ◎Promotion display not confirmed
- ✓Procedure: damaged stock returns
- →Top task today: replenish front aisle
Some of the most valuable retail information is visible.
- Q
What’s on the shelf?
- Q
What’s missing?
- Q
Is the display correct?
- Q
Is a promotion actually present?
- Q
How much shelf space does a product occupy?
- Q
What changed since the last visit?
- Image
- Detect
- Identify
- Compare
- Structure
- Action
Retail is more than selling products.
Behind every transaction is an operation — and AI can potentially improve processes across that operating model.
- Suppliers
- Purchasing
- Inventory
- Stores
- Customers
- Orders
- Suppliers
- Inventory
- Warehouses
- Stores
- Employees
- Customer requests
- Documents
- Returns
- Pricing
- Product information
Customers see the store. Employees see the administration behind it.
- Product information
- Supplier documents
- Invoices
- Price changes
- Customer emails
- Returns
- Claims
- Order processing
- Reports
- Approvals
- 01Email / document / request
- 02Understand
- 03Extract
- 04Validate
- 05Apply rules
- 06Update system
- 07Human exception
Automate the predictable. Escalate the exceptional.
The answer shouldn’t depend on who happens to be working that shift.
Retail organizations accumulate knowledge across product documentation, store procedures, return policies, supplier information, promotional rules, training materials and internal communications. AI can retrieve answers from approved organizational knowledge.
Give every employee access to the knowledge of the organization — at the moment they need it.
- “Can this product be returned?”
- “What’s the procedure for damaged stock?”
- “Which products can substitute for this item?”
- “When does this promotion end?”
- “What should I do with this type of customer complaint?”
Some of your most valuable retail knowledge has never entered a database.
- 01
An experienced store manager knows which signals mean trouble.
- 02
A category manager knows why two apparently similar products behave differently.
- 03
A buyer knows which supplier issue deserves immediate attention.
- 04
A salesperson knows how a particular customer behaves.
- 05
A regional manager knows what separates a strong location from a weak one.
Your transactions show what customers did. Your people often understand why.
Explore CaptaThe customer sees one retailer. Your systems should too.
A customer’s relationship with a retailer may span many touchpoints — but the underlying information can remain fragmented. AI becomes more useful when it works with relevant context across those interactions.
- Physical stores
- E-commerce
- Mobile applications
- Customer service
- Loyalty
- Delivery
- Returns
- Promotions
Channels are an organizational distinction. Customers experience one business.
Intelligence becomes useful when it connects to the systems that run the business.
Relevant information may live across ERP, POS, e-commerce, CRM, WMS, PIM, loyalty, inventory and pricing systems, data warehouses, supplier systems and custom applications.
The goal isn’t another AI destination. It’s intelligence inside the retail operation.
Explore IntegrationsThe more AI understands about your customers and operations, the more important its architecture becomes.
Depending on the requirements, Margins can design AI around customer-controlled cloud, private and on-premise environments.
Explore Private & On-Premise AI- Customer behaviour
- Transactions
- Pricing
- Margins
- Supplier information
- Inventory
- Commercial strategy
- Internal procedures
- Employee knowledge
Bring AI to your business data without unnecessarily moving your business data somewhere else.
Automate the question. Not the relationship.
A large proportion of customer requests may be repetitive. AI can understand the request, retrieve relevant information and resolve straightforward cases while escalating exceptions.
Remove repetitive service work so people can spend more time on the interactions that actually require people.
“We have that problem.”
Hover or tap any part of the retail operation to see the AI opportunities worth investigating there.
- Exception detection
- Forecasting
- Commercial intelligence
- Supplier intelligence
- Document processing
- Demand signals
- Supplier intelligence
- Planning support
- Demand forecasting
- Stock-risk detection
- Replenishment signals
- Task prioritization
- Store intelligence
- AI assistants
- Visual inspection
- Personalization
- Customer-service automation
- Behaviour signals
- Churn / retention
- Basket analysis
- Assortment intelligence
- Product relationships
- Uplift analysis
- Cannibalization
- Customer response
- Document processing
- Data entry
- Email automation
- Customer context
- Relevant offers
- Detect · predict · recommend
- Closed-loop measurement
Don’t start with “AI for retail.” Start with the problem worth solving.
We work with leadership and operational teams to understand where:
- Margin is leaking.
- Inventory decisions are difficult.
- Demand is hard to anticipate.
- Employees spend repetitive time.
- Customer behaviour is changing unnoticed.
- Stores lack useful information.
- Systems contain disconnected data.
- Management discovers problems too late.
- 01Business problem
- 02Process
- 03Data
- 04AI opportunity
- 05Value × Feasibility
- 06First implementation
The objective isn’t to find somewhere to use AI. It’s to find where AI is worth using.
Retail doesn’t need to become AI-first overnight.
Start with one valuable problem. Every successful implementation creates data, integrations and organizational capability for the next.
- 01DemandImprove forecasting.
- 02InventoryImprove availability.
- 03MerchandisingUnderstand product relationships.
- 04StoresSurface operational exceptions.
- 05CustomersMake interactions more relevant.
- 06AutomationRemove repetitive work.
- 07KnowledgePut company expertise to work.
Intelligence has limited value if nothing happens because of it.
- 01Observe
Transactions, inventory, customers, products and operations.
- 02Detect
Identify what changed.
- 03Understand
Determine why it matters.
- 04Decide
Recommend what should happen.
- 05Act
Put the decision into the workflow.
- 06Measure
See whether the action changed the result.
- 07Learn
Use the outcome to improve the next decision.
The goal isn’t better analytics. It’s a business that responds better to what the analytics reveal.
We build intelligence into the operation — not another tool beside it.
Business first
Start with the commercial or operational outcome.
AI + software
Build both the intelligence and the systems employees actually use.
Predictive AI + computer vision + LLMs
Use the appropriate technology for the problem rather than forcing everything through generative AI.
Enterprise integration
Connect AI to the existing retail technology environment.
Private architecture
Design around customer-controlled infrastructure where required.
Production ownership
Forward Deployed Engineering and Managed AI keep systems close to business reality after go-live.
Adjacent proof — not relabelled as retail.
Transaction history surfacing commercial signals before they become obvious.
Physical-location activity and commercial data turned into actionable field intelligence.
Outlet-specific strategy, AI assistance, computer vision and management visibility across field-sales operations.
See the outcome →Related capabilities
Predictive AI
Find patterns in customer, product and operational data.
Explore Predictive AI →Computer Vision
Turn physical retail environments into structured information.
Explore Computer Vision →Intelligent Automation
Remove repetitive information work across retail operations.
Explore Intelligent Automation →Integrations
Connect AI to POS, ERP, e-commerce, inventory and customer systems.
Explore Integrations →Enterprise Knowledge & RAG
Put organizational knowledge in the hands of employees.
Explore Enterprise Knowledge →
Your business creates millions of signals. Which ones could change what you do next?
We’ll work with your leadership and operational teams to identify where AI can materially improve commercial performance, inventory, customer experience or operations — and determine which opportunity is worth building first.
Explore AI opportunitiesFind the signal. Make the decision. Measure the outcome.