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E-commerceAI implementation case study
E-commerce – Quick-Commerce Grocery (Metro Hubs): Dark-Store Fulfillment AI
AI for pick-path optimization, substitution intelligence, rider dispatch, and customer promise recovery.
This case study documents a real client implementation. Identifying details may be withheld where confidentiality applies.
Key takeaway
PrimeAxiom automated dark-store operations end-to-end—warehouse, last mile, and customer recovery—in one AI stack.
The engagement
From operational friction to a connected AI system
01 · Context
Business overview
Industry: q-commerce grocery. Complexity: 15-minute promise windows.
02 · Challenge
Core problem
Manual picking, bad subs, rider chaos at peak.
03 · Approach
Solution
PrimeAxiom deployed fulfillment AI: real-time pick routing agents, substitution models with dietary constraints, rider batching optimization, and proactive customer credit agents.
04 · Impact
Results
Units per labor hour +19%; substitution acceptance +27%; on-time delivery +16%; support contacts per order −35%.
System design
The full automation system
The implementation connects triggers, AI decisions, business rules, human approvals, and downstream actions in one controlled workflow.
Pick Agent, Substitution Agent, Rider Agent, Recovery Agent.
Workflow
How the automation runs
1. Order wave.
2. AI builds pick paths.
3. Stock checks; subs offered.
4. Riders batched.
5. Delay predictions trigger CX.
6. Humans handle outages.
7. Models retrain on outcomes.
Intelligence layer
AI agents and decision logic
Routing Agent, Sub Agent, Dispatch Agent, CX Agent.
Under the hood
Technical architecture
Low-latency orchestration, inventory sync, LLM for sub explanations.
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