RETAIL INTELLIGENCE / DECISION SUPPORT
Turn Retail Data Into Decisions.
Combrain turns fragmented retail data into context, intelligence, decisions and actions.
Built on top of the systems you already use.
USER / HUMAN
Asks one question
COMBRAIN
COMPREHENSIVE RETAIL DECISION PLATFORM
INTERPRETS THE QUESTION · REASONS ACROSS SOURCES
RETAIL LOGIC
DETERMINISTIC MODELS
ANALYTICS
FORECASTING
OPTIMIZATION
BUSINESS CONTEXT
LLM REASONING
EXISTING SYSTEMS & DATA / OPERATIONAL SOURCES
ERP
POS
WMS
E-COMMERCE
CRM
BI
PLANNING
SPREADSHEETS
EXTERNAL DATA
Keep your systems. Add intelligence.
Combrain works across the retail technology stack you already have.
PRODUCT EXPERIENCE
Ask complex retail questions in natural language. Combrain combines deterministic analytics, retail logic and LLM reasoning to investigate the answer.
COMBRAIN / CONVERSATION
ILLUSTRATIVE PRODUCT INTERACTION — NOT CUSTOMER RESULTS
YOU
Sales in the West region are above plan. Is there anything I should actually be worried about?
COMBRAIN
Yes. The topline is hiding an inventory mix problem.
Sales are 6.8% above last year, but 31% of inventory value across 23 stores is sitting in products whose velocity has declined for three consecutive weeks.
At the same time, 8 stores are short on high-velocity products, while demand remains healthy when availability is sufficient.
The risk is not demand. It is inventory allocation.
Before buying more inventory, I would investigate transfers from 11 overstocked stores into the constrained locations.
23 stores analysed
31% slow-moving inventory
8 availability gaps
11 transfer candidates
View Evidence
Explore Stores
Simulate Transfer
Fast
Deep Analysis
Send
THE INTELLIGENCE MODEL
More Than an LLM.
LLMs are powerful reasoning interfaces. Retail decisions also require mathematics, rules, models and evidence.
01 / DETERMINISTIC INTELLIGENCE
Metrics · Retail mathematics · Business rules · Statistical models · Forecasting · Optimization · Anomaly detection
02 / BUSINESS CONTEXT
Product hierarchy · Store characteristics · Retail calendar · Promotions · Weather · Historical behaviour · Company-specific rules
03 / LLM REASONING
Natural-language interaction · Cross-domain investigation · Hypothesis generation · Explanation · Decision synthesis
GROUNDED DECISIONS
DATA → CONTEXT → INTELLIGENCE → DECISION → ACTION
OBSERVE
What happened?
DIAGNOSE
Why did it happen?
PREDICT
What happens next?
DECIDE
What should we do?
SIMULATE
What if we do it?
ACT
Execute approved actions.
INDUSTRY-REPORTED OUTCOMES
The Opportunity Is Measurable.
Industry research and reported retail implementations show what better forecasting, inventory intelligence and AI-supported decisions can unlock.
10–20%
LOWER INVENTORY
Reported in AI-enabled supply-chain and inventory optimization use cases.
UP TO 30%
FEWER STOCKOUTS
Reported in predictive assortment and AI-enabled supply-chain implementations.
2–5%
SALES UPLIFT
Reported across selected AI-enabled merchandising and retail decision use cases.
10–20%
IMPROVED FORECAST ACCURACY
Reported in advanced demand forecasting use cases.
Reported outcomes vary by retailer, use case, data quality and implementation maturity.
PLATFORM CAPABILITIES
Intelligence for every retail decision.
AI agents continuously analyse, investigate and prepare actions across your retail operations.
BUSINESS QUESTION → COMBRAIN AGENT → MODELS + CONTEXT → RECOMMENDATION → HUMAN APPROVAL / CONTROLLED WORKFLOW → OUTCOME
● PERFORMANCE AGENT / ACTIVE
Retail Performance Intelligence
Continuously investigates anomalies and performance changes.
Detect anomaly → Benchmark → Investigate context → Identify drivers → Surface action
Why is this store growing revenue but losing contribution?
● INVENTORY AGENT / ACTIVE
Inventory Intelligence
Continuously detects trapped capital, excess stock, missed demand and transfer opportunities.
Monitor stock → Detect imbalance → Investigate → Recommend transfer → Request approval
Where should we move inventory before buying more?
● ALLOCATION AGENT / ACTIVE
Allocation Intelligence
Continuously evaluates where products have the highest probability of selling.
Analyse demand → Compare context → Rank destinations → Recommend → Approved action
Which stores should receive this product next — and why?
● REPLENISHMENT AGENT / ACTIVE
Replenishment Intelligence
Continuously prioritises replenishment based on expected business impact.
Monitor availability → Detect risk → Estimate impact → Prioritise → Trigger workflow
Where will we lose the most sales if we do nothing?
● MERCHANDISING AGENT / ACTIVE
Merchandising Intelligence
Continuously evaluates assortment, product performance and distribution.
Monitor assortment → Identify patterns → Analyse fit → Recommend → Track outcome
Which products appear successful only because we keep pushing them?
● CONTEXT AGENT / ACTIVE
Contextual Intelligence
Continuously enriches retail decisions with business and external context.
Collect context → Connect events → Compare conditions → Explain → Inform agents
Would this store still underperform after controlling for weather, traffic, assortment and stock?
FOR RETAIL LEADERS
Questions you should be able to ask instantly.
What should my team focus on this week?
Where is inventory tying up capital without creating sales?
Where will we lose sales if we do nothing?
Why is this store growing revenue but falling in contribution?
Which products should we move between stores before buying more?
If we reduce inventory by 15%, where does availability become risky?
Which stores are genuinely underperforming after controlling for context?
What changed since last week — and what actually matters?
CONTROLLED AUTOMATION
From Decision to Action.
Start with decision support. Automate progressively as trust grows.
Replenishment recommendation / Stock transfer proposal / Exception workflow / Task creation / Approved system write-back
THE NEXT DECISION STARTS HERE
Combrain brings data, context, models and AI reasoning together in one retail decision platform.