Retail AI Architecture
The retail AI engine behind every CINDE decision
60+ predictive and diagnostic models. 20+ specialized AI agents. A retail knowledge graph with 170+ entity types. Purpose-built for retail, not adapted from generic AI.
Most retail AI projects don't scale
Generic AI platforms ship with empty ontologies. Before they can answer a retail question, your team spends months building data models and training on retail-specific patterns. CINDE is retail-native. The ontology already understands product hierarchies, promotional mechanics, and shelf-to-sales linkage. Your team configures and extends from day one.
CINDE's AI engine operates in a continuous six-stage loop
Each stage feeds the next, and outcomes from the final stage feed back into the first.
1. Detect
What is happening?
Multi-method anomaly detection (z-score, IQR, distributional change-point analysis), trend detection with slope significance testing, and shelf image recognition via computer vision. The system finds problems before users ask.
2. Attribute
Why did it happen?
Causal driver identification using debiased ensemble inference (Double ML, Causal Forests). Shapley-value metric decomposition guaranteeing 100% attribution across volume, price, and mix. Feature importance ranking via SHAP. No unexplained residuals.
3. Predict
What will happen next?
14 proprietary forward-looking models covering promotion effectiveness, price impact, demand transfer, new item forecasting, on-shelf availability prediction, customer lifetime value, and scenario simulation. Each model operates within the shared knowledge graph for cross-lever reasoning.
4. Simulate
What if we tried this?
Digital twin simulation modeling cross-lever P&L impact (price, promo, assortment, space) before execution. Monte Carlo stochastic simulation with learned effect priors and competitive response dynamics. Test any decision virtually before committing real resources.
5. Optimize
What should we do?
Multi-objective optimization across competing goals (sales, margin, CLV) under real-world constraints (budget, competitive position, frequency limits). Cross-lever optimization that prevents conflicting recommendations across price, promo, and assortment.
6. Learn
How do we improve?
Bayesian online updating refines effect estimates with every action taken. Backtesting validates predictions against historical outcomes. Prediction monitoring catches model drift before it reaches users. Attribution validation ensures every causal claim passes a minimum explained variance threshold (70% or higher) before surfacing.
The retail knowledge graph
CINDE's knowledge graph is the foundational data layer
It replaces the traditional approach (ETL pipelines feeding a monolithic database) with a multi-model graph database connected to internal and external data sources through connectors and APIs.
– 170+ entity types
– 350+ relationship types
– Organized into a canonical retail ontology
Every model and every agent operates on the same shared ontology
A promotion effectiveness model and an assortment optimization model see the same product, the same store, the same customer segment. This eliminates the data reconciliation problem that plagues multi-vendor analytics stacks, where different tools define "category" or "store cluster" differently and produce conflicting recommendations.
Four data hubs
Pre-built for retail. Connected by design.
Product Hub
Connects:
Brand, category, pricing, supplier, lifecycle
Customer Hub
Connects:
Segment, household, loyalty, preferences
Store Hub
Connects:
Geography, department, fixture, planogram
Supply Chain Hub
Connects:
Warehouse, shipment, carrier, quality
Key services: What the retail AI knowledge graph delivers
Entity resolution
Deduplication and linking across sources — so the same product, store, or customer is always one entity, not dozens of variants across systems.
Semantic query routing
Queries are routed to the right part of the graph automatically — no manual mapping or translation layer required between tools.
SPARQL / GraphQL API
Standard APIs so models, agents, and external tools can query the knowledge graph without custom integration work for each connection.
Explainability hub with reasoning traces and full audit trail
Every recommendation is traceable to its data source and decision logic — supporting compliance, reviews, and buyer confidence.
Time series versioning
The graph is versioned over time — models can query historical states of data, not just the current snapshot.
Data lineage tracking
Full traceability from raw source to model input — supporting audits, debugging, and regulatory accountability.
Real outcomes, proven in production
$182M incremental profit
validated at a top U.S. grocery retailer, powered by CINDE's predictive and diagnostic models across ~300 AI-driven projects
70% reduction in analysis time
at Schnucks, where category reviews dropped from 5.5 hours to 1 hour per category using CINDE's AI assistants
+6 points on-shelf availability improvement
average across deployments, driven by CINDE's computer vision pipeline and predictive OOS models