Agent-User
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Enterprise scenario

Retail

Covers core retail scenarios such as smart recommendation, dynamic pricing, inventory forecasting, and store operations; integrates online and offline data to drive business growth.

↑ 28%
Conversion rate
↑ 35%
Inventory turnover
↑ 18%
Average order value

Industry challenges

  • User profile depends on rules, hard to personalize
  • Dynamic pricing lacks data support
  • Inventory forecasting is inaccurate, stock-out and overstock coexist
  • Store operations depends on store manager experience
  • Online and offline data is siloed

Agent-User solutions

Smart recommendation

Combines user behavior + product features + context, real-time personalized recommendation; supports A/B testing and effect tracking.

Dynamic pricing assistant

Competitor prices + inventory + user sensitivity + campaign strategy, smartly gives pricing suggestions and profit forecast.

Inventory forecast

Historical sales + seasonality + promotions + weather data trained, SKU-level inventory forecast accuracy 90%+.

Store operations assistant

Store manager can ask on the spot: today's sales, hot products, pending items, employee scheduling suggestions.

Agents involved

Recommendation agentPricing assistantInventory forecast agentStore operations agent

Case study note

Applicable to chain supermarkets, branded retail, and e-commerce platforms, covering apparel, beauty, fresh, 3C and other categories.

Want to deploy in this industry?

We provide industry solution design + POC validation + go-live accompaniment end-to-end service.