dimaspanduOverview
Turning catalog data into stocking decisions.
I added AI-driven analytics to a product catalog system to improve inventory decisions and sales targeting.
The layer connects what products exist with how they actually sell, then surfaces what to do next.
The problem
Teams saw what sold, not why or what to stock.
Product teams had visibility into what was selling, but not why or what to stock next.
- Sales reports described the past without pointing at the next action.
- Inventory decisions were made on gut feel rather than demand signals.
Approach
Surface trends and demand signals with AI.
I integrated AI analytics to surface trends, demand signals, and product performance insights.
The model was pointed at catalog and sales data together so insights reflected the full picture, not a slice.
Solution
An analytics layer that recommends, not just reports.
The solution is an analytics layer that connects product catalog data with sales patterns and generates actionable recommendations.
Instead of static charts, teams get suggestions they can act on for inventory and targeting.
Implementation
AI integration and a dashboard on top of the catalog.
The work was implemented with AI integration, an analytics dashboard, catalog management, and a data pipeline.
AI
Analytics integration surfacing trends and demand signals.
Dashboard
Product performance insights and recommendations UI.
Data
Catalog management and a sales data pipeline.
Results
What changed.
Inventory planning improved and the team could identify high-potential product segments more confidently.
Links