Dynamic Pricing and Promotion Recommendation Agent
Retail GenAI case study for faster price and promotion decisions on perishable inventory.
About Customer
The customer is a retail enterprise operating high-velocity grocery and FMCG categories with significant perishable inventory exposure. Store teams manage daily pricing and promotional decisions across multiple locations where margin protection and waste control are both critical.
Leadership needed a faster and more consistent decision framework that could blend pricing intelligence, policy controls, and live stock conditions in one operational workflow.
Problem Statement
Each morning, store managers had to manually compile competitor prices, corporate guidelines, and stock positions before deciding what to discount and what to protect. By the time these checks were complete, high-demand windows had often passed and inventory risk had already changed.
The team was forced into reactive pricing behavior: some SKUs were discounted too late, others too aggressively, and perishable items often moved into markdown cycles that hurt profitability and increased waste.
- Pricing and promotion updates were too slow for perishables.
- Manual cross-referencing across policy, competitor, and inventory data.
- Resulted in increased waste and lower profitability.
Solution Architecture
Zettabolt deployed a specialized Dynamic Pricing Agent that combines RAG (Retrieval-Augmented Generation - the AI looks up relevant policies before answering) over the customer's corporate pricing rules with ZettaLens-built secure connectors to real-time inventory and competitor pricing APIs. The agent fuses these live signals on demand to recommend the optimal daily price or promotion per SKU per store - cutting wastage up to 25%, lifting margin up to 15%, and accelerating the price/promotion adjustment cycle 80X. Here is how we integrated the pipeline:
Implementation Highlights
- Built a specialized pricing agent using LLM + RAG for policy-grounded decisions.
- Integrated real-time inventory via secure ZettaLens tool use.
- Automated optimal daily price/promotion recommendations for store teams.
Implementation context: The rollout started with a controlled set of perishable categories and policy guardrails, then expanded store-by-store as confidence increased. Teams used AI-generated recommendations with manager-in-the-loop approval, which balanced automation speed with commercial accountability and improved adoption across operations, merchandising, and pricing governance.
Business Impact
