Imagine all your sales, inventory, and retail partner reports arriving as messy, different-shaped puzzle pieces every day. This application is like a smart assistant that collects those pieces from every retailer, cleans them up, snaps them into one big picture, and then circles in red where you’re about to run out of stock, where you’re losing shelf space, or where promotions are working best.
CPG brands and their retail partners are drowning in fragmented, inconsistent data from multiple retailers, distributors, and internal systems. This fragmentation makes it slow and difficult to answer basic questions like: What are true sales by product and region, where are we out-of-stock, and which promotions are driving incremental lift? The use case solves for unifying retail data into a single, timely view and layering analytics/AI on top so demand, replenishment, and trade decisions are faster and more accurate.
Tight integration pipelines with major retailers and distributors, domain-specific data models for CPG/retail, and embedded workflows for sales, category management, and demand planning that make the platform sticky. Longitudinal, proprietary harmonized datasets across retailers also become a defensible asset over time.
Hybrid
Vector Search
Medium (Integration logic)
Retail data onboarding and harmonization across many partners; ongoing maintenance of connectors and schema mappings likely dominate complexity and cost.
Early Majority
Differentiation comes from deep specialization in CPG/retail data schemas, prebuilt connections to major retailers, and workflows specifically tuned for sales, category management, and supply-chain teams rather than generic BI or generic AI analytics.