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E-commerceAI implementation case study
E-commerce – DTC Fashion Brand ($120M GMV): Demand & Fulfillment Intelligence AI
Full AI automation for demand sensing, PO recommendations, 3PL orchestration, and CX recovery workflows.
This case study documents a real client implementation. Identifying details may be withheld where confidentiality applies.
Key takeaway
PrimeAxiom unified merchandising, supply chain, and customer recovery in one AI-orchestrated operating system.
The engagement
From operational friction to a connected AI system
01 · Context
Business overview
Industry: DTC apparel. Complexity: seasonal collections, influencer spikes.
02 · Challenge
Core problem
Manual forecasting, slow PO cycles, CX disconnected from inventory.
03 · Approach
Solution
PrimeAxiom deployed a commerce brain: AI demand models per SKU, autonomous PO drafts within guardrails, 3PL routing agents, and CX agents issuing refunds/exchanges with margin awareness.
04 · Impact
Results
Stockout rate −31%; margin on promotions +2.3 pts; CX resolution time −44%; planner manual hours −52%.
System design
The full automation system
The implementation connects triggers, AI decisions, business rules, human approvals, and downstream actions in one controlled workflow.
Demand Agent, PO Draft Agent, 3PL Router Agent, CX Resolution Agent.
Workflow
How the automation runs
1. Signal ingestion: sales, ads, weather.
2. AI forecasts by SKU.
3. PO suggestions.
4. Finance approves thresholds.
5. 3PL pick/pack updates.
6. CX issues tied to stock positions.
7. Continuous learning.
Intelligence layer
AI agents and decision logic
Forecast Agent, Inventory Agent, Logistics Agent, CX Agent.
Under the hood
Technical architecture
Data warehouse, real-time Shopify hooks, LLM for CX with policy engine.
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