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 data from your existing systems. CINDE supports structured feeds (flat files, APIs, database connections) and streaming data. External data sources (market data, competitive signals, weather, economic indicators) connect through the same framework. No centralized data warehouse required.
2. Unify
CINDE’s intelligence layer normalizes and resolves entities across sources. A product in your POS system, your planogram tool, and your supplier portal becomes one product with a unified view. This unification happens inside CINDE; it does not require you to clean or restructure your source data.
3. Act
AI assistants and predictive models generate recommendations that surface inside your existing tools, not in a separate application your teams have to learn. Insights route to the merchant’s BI environment, the store manager’s task system, or the supply chain planner’s workflow.
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. There is no migration project, no re-platforming, no parallel run period. Your core transactional systems continue to operate exactly as they do today. CINDE adds the intelligence layer they're missing.
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 additional use case connects to the same underlying intelligence layer, so the second deployment is faster than the first. This means the business case doesn't need to justify the full platform on day one.
Retail-specific data model
CINDE's data layer is built on a retail ontology with 170+ entity types and 350+ relationship types. This matters because generic AI platforms require significant custom data modeling before they can process retail-specific relationships (product hierarchies, promotional mechanics, store clustering, shelf-to-sales linkage). CINDE ships with those relationships pre-built.
Open platform for extensibility
CINDE supports custom data connectors, custom models alongside its 60+ proprietary models, and third-party agent integration. Your data science team can extend the platform without being locked into a single vendor's model library. APIs support embedding CINDE intelligence into your own applications and dashboards.
Enterprise security and governance
Data lineage, audit trails, role-based access, and reasoning traces are built into the platform. Every recommendation can be traced back to the data and model that produced it. This matters for retailers operating under data governance requirements or managing supplier data access.
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.