Buyer and Merchandise Agent
LLM + RAG + NLP + SQL + AI Agent for retail data democratization and faster decisions.
About Customer
The customer is a leading fashion retail organization with centralized analytics teams and distributed buying and merchandising functions. Category managers and planners regularly needed answers on sales, inventory, and trend signals to make assortment and procurement decisions.
However, business users lacked direct access to data exploration and depended heavily on technical teams for query formulation and dashboard modifications.
Problem Statement
Merchandise and buying teams had urgent questions every day, but answers depended on analyst bandwidth and SQL translation cycles. Even simple commercial queries often moved through tickets, clarifications, and rework before results were usable.
A typical customer story involved a category manager preparing weekly assortment decisions. The manager needed sell-through, stock cover, and competitor trend context before approving buys, but each answer required a separate analyst request and follow-up interpretation. By the time outputs were consolidated, the buying window had narrowed and decisions were made with partial context.
This slowed decision-making during key planning windows, while external market shifts continued in parallel. Teams could not consistently combine internal performance data with competitor and trend context at the speed needed for tactical action.
- Heavy dependence on analysts for query creation and interpretation.
- Limited access to contextual market trends in day-to-day workflows.
- Low data democratization across buying and merchandise teams.
Solution Architecture
Zettabolt deployed an AI agent built with ZettaLens that converts plain-language buyer questions directly into SQL database queries - no analyst handoff needed. It then enriches the results with external market context using RAG (Retrieval-Augmented Generation) over competitor data, fashion blogs, and trend reports. Embedded directly in Microsoft Teams, it puts live data within reach of every buyer and merchandiser: answers arrive as a chart or short narrative inside their daily chat, 100X faster than the old analyst-bottlenecked path, with 100% on-demand market context and up to 10% better purchasing decisions. Here is how we integrated the pipeline:
Implementation Highlights
- Deployed an intelligent NLP-to-SQL query agent backed by RAG and KPI-aware schema guidance.
- Integrated external market context for richer decision support beyond internal sales and inventory metrics.
- Added response guardrails to reduce ambiguous queries and improve trust in self-serve insights.
- Delivered chart and narrative output directly in user workflows (Microsoft Teams) for immediate action.
Implementation context: The deployment focused first on high-frequency retail queries and governed KPI definitions so that generated SQL stayed aligned with business logic. The team embedded responses directly in collaboration workflows, which reduced context switching and helped non-technical users adopt data-driven decisions without waiting for analyst queues.
Business Impact
Buying teams moved from ticket-driven analytics to in-flow decision support, improving reaction speed during assortment, pricing, and replenishment cycles.
