Planogram compliance failures, undetected out-of-stocks, and the lag between headquarters planning and store-level execution cost grocers measurably in comp sales, shrink, and associate labor hours. The tools built for category planning have never been designed to close that execution gap. In response, SymphonyAI, a global leader in Vertical AI, announced the availability of CINDE Assortment and Space for CPGs, an AI platform that closes the loop between assortment strategy, planogram execution, and in-store compliance, compressing the category review cycle from four to six weeks to a matter of days.

80% of CPG headquarters lack the analytics needed to support assortment and shelf decisions — still running quarterly reviews on stale spreadsheets while category performance erodes between resets.

The platform is underpinned by SymphonyAI’s Transferable Demand AI, a model trained across 25 years of retail data and validated in production across more than 500 global CPG deployments, including PepsiCo, Southern Co-op, and every one of the top 25 global CPG companies. CINDE Assortment and Space is available as a standalone Assortment-as-a-Service offering and as part of the full CINDE CPG Enterprise Suite.

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Five Use Cases

Each addresses a specific, recurring failure point in how CPGs manage category decisions today, identified across decades of production deployments:

• Store Intelligence — Photo-to-Planogram Loop: A merchandiser takes a mobile photo of the shelf. Computer vision identifies every SKU, facing, and out-of-stock condition and routes a corrective task to the right associate, with no manual audit, no separate compliance tool, and no lag between the store walk and the action. The output feeds directly into the next assortment cycle, closing the planogram compliance gap that compounds between resets and drives on-shelf availability loss.
• Assortment Optimization — Transferable Demand: SKU-level rationalization driven by incrementality scoring, need-state analysis, and cannibalization modeling. Southern Co-op achieved a 5.2 percent category sales uplift using this capability, a comp sales outcome driven by getting the right SKUs on the right shelves. The Transferable Demand AI model carries across retailers without retraining, meaning work validated at one JBP extends to the next without rebuilding from scratch.
• Intelligent Store-Based Clustering: Store clustering derived from store-level transaction and sales patterns, enabling highly accurate, scalable models tailored to localized demand, shopper behavior, and operational dynamics. This capability is particularly valuable across wholesale environments and markets with limited customer-level data, including many Asia-Pacific and emerging markets where retailer adoption depends on practical, store-centric intelligence.
• Space-Aware Assortment — Category Captain Workflow: Integrated assortment and planogram planning from a shared data model, producing shelf-ready outputs that store teams can act on directly. These are not analytical recommendations requiring manual translation before execution. PepsiCo deployed this capability for category captain planning across major UK grocery retailers, compressing range review cycles and generating retailer-ready deliverables within the platform.
• Planogram Automation — Multi-Banner Scale: Store-specific planograms generated and maintained at scale across banners, countries, languages, and regulatory contexts, eliminating the manual rebuild cycle that consumes field and category team capacity between resets. A global retailer based in Europe runs CINDE planogram automation across 32 countries and 32 banners, the largest multi-banner deployment of its kind.

Market Context

According to the POI 2024 State of the Industry Report, 80 percent of CPG headquarters lack the capabilities needed to support pricing, trade, and go-to-market decisions. The category has historically been served by disconnected point solutions: clustering, planogram, and compliance tools that operate in isolation, each requiring manual handoffs that introduce lag and execution failures at store level. No vendor has previously integrated all four disciplines into a single platform with a shared data model.

The convergence of three factors has made this integration both technically feasible and commercially urgent: the acceleration of range review frequency as retailers demand more responsive joint business planning; the availability of computer vision at scale for in-store compliance verification; and the maturation of large-scale demand models capable of transferring across retail contexts without retraining.

“CPGs have spent years making assortment and shelf decisions with disconnected tools that cannot share data across the planning cycle,” said Manish Choudhary, President of Retail, SymphonyAI. “CINDE Assortment and Space was built to close that loop entirely. When a demand model proven at one retailer transfers without retraining to every other JBP in the portfolio, and when in-store execution feeds directly back into planning, CPGs stop rebuilding work from scratch every quarter. That capability is available today, backed by 25 years of production deployment across more than 500 global CPGs.”