01
Need
Rapid growth strained the startup's supply chain. Forecasts generated in spreadsheets missed local promotions, leading to frequent stock-outs and markdowns.
D2C Retail Startup
Optimising inventory with demand forecasting and conversational assistance
Series C direct-to-consumer retailer with multiple fulfilment centres. Deployed in 8 weeks.
-25%
Stock-outs
+10%
Gross margin
10%
Incremental annual revenue
Story
Need, build, outcome.
02
Our Build
Our team built a forecasting engine that blends historical sales with real-time signals such as events, weather, and marketing, then surfaces recommendations through a chat-based assistant. Store and warehouse managers ask how much they should order and receive narrative guidance. Automated replenishment workflows trigger purchase orders and allocate inventory across the network.
03
Outcome
Stock-outs fell by 25% and gross margin improved by 10%. The retailer captured an estimated US$8 million in additional annual revenue while freeing managers to focus on customer experience.
"The AI assistant tells us what to order and why; it's like having a planning team in our pocket."
Operations lead, D2C retailer
Highlights
What the system changed.
Forecasting Engine
Historical sales blending
Real-time signal ingestion
Promotion-aware demand planning
Inventory allocation recommendations
Conversational Operations
Chat-based planning assistant
Narrative order guidance
Automated replenishment triggers
Warehouse and store manager workflows