Specialized Platform Agents
Retail-trained AI agents operate across forecasting, merchandising, pricing, inventory, promotion, store execution, and intelligence. Each specializes in a specific decision while sharing the context required to work as part of one connected plan.
Retail Automation Platform
Build and deploy custom AI agents without creating another disconnected layer of automation. The no-code Retail Automation Platform grounds agents in your enterprise data, permissions, and workflows while placing them under the same governance and orchestration as the rest of the platform.
CortexEye™
CortexEye provides the decision intelligence behind the agent system, interpreting business context, identifying performance drivers, and giving agents an explainable foundation for recommendations and actions.
Agentic For
Forecasting
Demand forecasting agents continuously sense changes in customer behavior and create the demand signal other retail decisions rely on.
Agentic For
Pricing
AI pricing agents coordinate everyday prices, promotions, and markdowns against the demand and inventory conditions surrounding each decision.
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Agentic For
Inventory
AI inventory agents use current demand, pricing, availability, and service-level objectives to determine where inventory should sit and where action is required.
Agentic For
Data & Decision Intelligence
AI decision intelligence agents connect performance signals across the business, helping every function understand what changed and why.
Demand forecasting agents continuously sense changes in customer behavior and create the demand signal other retail decisions rely on.
AI pricing agents coordinate everyday prices, promotions, and markdowns against the demand and inventory conditions surrounding each decision.
AI inventory agents use current demand, pricing, availability, and service-level objectives to determine where inventory should sit and where action is required.
AI decision intelligence agents connect performance signals across the business, helping every function understand what changed and why.
Identify where agentic AI can create the greatest value, evaluate data and workflow readiness, and prioritize the decisions where autonomy can deliver measurable business impact first.
Create agents on your own business data and workflows using retail-trained intelligence and a no-code development environment. Custom agents inherit enterprise permissions, business rules, and orchestration from the start.
Release agents into governed production with testing, security, observability, and approval controls already in place. Custom agents can collaborate with Impact Analytics platform agents rather than becoming another isolated automation.
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Merchandising, pricing, inventory, and allocation agents continuously share context, keeping store-level decisions aligned as demand changes.
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Demand, replenishment, pricing, and shelf-life decisions work together so high-velocity and perishable inventory can respond before availability or shrink is affected.
Demand forecasting agents carry shifts into procurement, inventory, and production planning before commitments are locked, helping reduce excess cost and supply risk.
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Changes in account demand flow into inventory and allocation decisions across the remaining book of business, helping teams preserve commitments as conditions shift.
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Demand, pricing, staffing, and operational signals can be coordinated by daypart and location so teams respond to changing traffic before service or margin deteriorates.
Autonomous systems require more than intelligence. They require clear limits, traceability, and enterprise controls.
Impact Analytics agents operate within role-based permissions, administrator-defined guardrails, approval workflows, testing, security, and observability. Recommendations and actions carry the evidence and decision context behind them, giving enterprises control over what can execute autonomously and what requires human review.
In our clients’ own words
“This partnership allows us to eliminate repetitive, low-value tasks so our merchandisers can focus on understanding what is truly happening in our brand and make quicker, higher-quality decisions to get the right products in the right places for our customers.”
“We landed on Impact Analytics for 3 core reasons: culture, technology, and an intuitive interface that was easy to adopt.”
“Our planners immediately recognized that Impact Analytics understood the realities of retail planning at scale. The opportunity to move from spreadsheet-driven, line-by-line planning to an exceptions-management model is a significant step forward.”
