Modular Retail AI Architecture
Your systems stay. CINDE layers on top.
Connect your existing retail infrastructure to a unified AI layer without replacing core systems or migrating data.
CINDE doesn't replace what's working. It connects what's siloed.
Your ERP, WMS, and planning tools already do their jobs. What's missing is the intelligence layer that ties them together. CINDE sits on top of your existing stack and connects what's siloed. No migration. No rip-and-replace.
Data connectors, not data migration
CINDE connects to your existing data sources (POS, loyalty, product, store, promotion, inventory, DC) through pre-built connectors and APIs. Data stays in your systems. CINDE reads from them, enriches the signals through its retail-specific AI models, and writes recommendations back into the workflows your teams already use.
The integration model works in three layers
1. Connect
Pre-built connectors pull from your existing systems (POS, ERP, WMS, planning tools) plus external data (weather, competitive signals). No data warehouse required.
2. Unify
The intelligence layer resolves the same product across your POS, planogram tool, and supplier portal into one record. Your source data stays as-is.
3. Act
Recommendations surface inside the tools your teams already use: BI environments, store task systems, supply chain workflows. No new app to learn.
The retail AI platform built to work with your existing systems
No rip-and-replace. No custom data modeling. No parallel run period.
No system replacement required
CINDE sits alongside your current ERP, POS, WMS, and planning tools. No migration project, no re-platforming, no parallel run.
Start with one use case, expand from there
Deploy CINDE for a single capability (shelf gap recovery, category performance, promotion optimization) and expand as value is proven. Each new use case shares the same intelligence layer, so the second deployment is faster than the first.
Retail-specific data model
A retail ontology with 170+ entity types and 350+ relationship types ships pre-built. Generic AI platforms require months of custom modeling before they can process retail relationships like product hierarchies, promotional mechanics, and shelf-to-sales linkage.
Open platform for extensibility
Bring custom data connectors, custom models alongside CINDE's 60+ proprietary models, and third-party agents. APIs let you embed CINDE intelligence into your own applications.
Enterprise security and governance
Data lineage, audit trails, role-based access, and reasoning traces are built in. Every recommendation traces back to the data and model that produced it, which matters for governance audits and supplier data access controls.
Real outcomes, proven in production
$182M incremental profit
validated at a top U.S. grocery retailer, where CINDE connected merchandising, assortment, and category management data across ~300 AI-driven projects, layered on top of the retailer's existing infrastructure.
7,500 planograms managed by 2-3 FTEs
at Systembolaget (450 stores), where CINDE's automated planogram generation connected to existing space planning workflows, saving 17,000 hours per year.
+6 points on-shelf availability improvement
average across deployments, achieved by connecting store-level vision AI to existing inventory and merchandising systems without requiring changes to the retailer's replenishment infrastructure.