Sumori provides brand-specific intelligence infrastructure for retail, connecting internal commercial data with external market signals to surface the few priorities that matter most. The platform coordinates decisions across teams, agencies, or agents, then evaluates outcomes to continuously refine its understanding of how a particular brand responds. It transforms scattered retail data into a shared commercial memory that gets wiser with each decision.
Funding
Funding not disclosed
Founders
Product
Problem
Retail intelligence is fragmented across disconnected systems, teams, and agencies, making it difficult for brands to form a coherent view of their commercial state. Most software only reacts after a metric moves, leaving teams blind to emerging demand shifts and unable to ground decisions in how their specific brand actually operates.
Solution
Sumori builds a continuously learning intelligence layer that connects what is happening across a brand's store, customers, and market, then distills it into ranked commercial priorities with clear ownership. The platform observes both external market formation and internal commercial state, translates those observations into actionable decisions with trade-offs and impact assessments, and evaluates outcomes to update its model of how that brand responds. This creates a shared commercial memory that lets teams see earlier, decide with context, and learn from every action taken.
Target Audience
Primary customers are retail brand leadership teams, including commercial, merchandising, and strategy functions, who need to connect scattered intelligence into a single decision-making layer.
Features
- Probabilistic formation intelligence that aligns heterogeneous observations into temporally coherent, multi-source patterns rather than single spikes
- Brand-specific state estimation that filters market footprints through a brand's products, customers, baselines, and constraints to infer demand shifts
- Human-controlled decision intelligence that computes clear commercial decisions with action, reason, impact, risk, timing, and owner
- Continuous learning loop that evaluates outcomes and updates the brand's response model after every decision
- Coordination layer that supports human teams, agencies, or autonomous agents in executing decisions