When a mid-market e-commerce retailer processing 8,000 orders per month came to us, their operations team was stretched thin. Manual inventory updates, order routing decisions, customer service responses, and return processing consumed 160+ staff-hours per week. They needed automation that could handle the volume without sacrificing the personalized service their customers expected.
This case study walks through the entire engagement — from the initial workflow audit to the production deployment — and documents the measurable results after 90 days.
The Starting Point
The retailer operated across three sales channels: their Shopify storefront, an Amazon marketplace, and a wholesale portal. Each channel had its own inventory tracking, order management, and customer communication workflows. The problems were predictable:
- Inventory discrepancies — stock levels across channels drifted out of sync daily, leading to oversells and backorders
- Manual order routing — a team member reviewed every order to determine which warehouse should fulfill it based on proximity, stock levels, and shipping speed
- Slow customer responses — the support team averaged 6-hour response times, with common questions (tracking, returns, sizing) consuming 70% of their time
- Return processing bottlenecks — every return request required manual review, approval, label generation, and refund processing
The Automation Strategy
We identified four high-impact automation targets, ranked by effort-to-value ratio:
1. Inventory Synchronization
We deployed a real-time sync engine that connected Shopify, Amazon Seller Central, and the wholesale portal to a single source of truth. Stock level changes propagated across all channels within 60 seconds. Buffer stock rules prevented overselling during high-traffic periods.
2. Intelligent Order Routing
An AI-powered routing system replaced the manual review process. The system evaluated each order against warehouse stock levels, geographic proximity to the customer, carrier pricing, and delivery SLA commitments. Orders were automatically assigned to the optimal fulfillment center within seconds of placement.
3. Customer Support Automation
We implemented an AI agent that handled first-line customer inquiries across email and live chat. The agent could:
- Look up order status and provide tracking information
- Process straightforward return requests end-to-end
- Answer product questions using the retailer's knowledge base
- Escalate complex issues to human agents with full context
4. Return Processing Pipeline
The return workflow was automated from request to refund. Customers initiated returns through a self-service portal. The AI agent validated eligibility, generated prepaid shipping labels, tracked the return shipment, and triggered the refund upon warehouse confirmation.
Implementation Timeline
| Week | Milestone |
|---|---|
| 1-2 | Workflow audit and system integration mapping |
| 3-4 | Inventory sync engine deployment and testing |
| 5-6 | Order routing AI training and staging validation |
| 7-8 | Customer support agent deployment (shadow mode) |
| 9-10 | Return processing automation and end-to-end testing |
| 11-12 | Full production rollout and monitoring |
Results After 90 Days
Manual Task Reduction: 70%
The operations team went from 160 staff-hours per week of manual work to 48. The remaining manual work consisted of complex customer issues, vendor negotiations, and strategic inventory planning — tasks that genuinely required human judgment.
Customer Response Time: 6 Hours → 45 Seconds
The AI support agent responded to 72% of incoming inquiries without human involvement. Average response time dropped from 6 hours to under 1 minute. Customer satisfaction scores improved from 3.8 to 4.6 out of 5.
Inventory Accuracy: 89% → 99.2%
Real-time synchronization eliminated the daily stock discrepancies that had been causing oversells and backorders. The oversell rate dropped from 3.2% to 0.1%.
Order Fulfillment Speed: 2.1 Days → 1.3 Days
Intelligent routing consistently selected the nearest warehouse with available stock. Average delivery time improved by 38%, and shipping costs decreased by 15% due to shorter transport distances.
Return Processing: 5 Days → 8 Hours
End-to-end return processing (from customer request to refund) dropped from an average of 5 business days to under 8 hours for standard returns.
Key Lessons Learned
Start with data integration before AI. The biggest productivity gains came from connecting disconnected systems, not from adding intelligence. Without real-time inventory sync, no amount of AI could fix the oversell problem.
Shadow mode builds trust. Running the customer support agent in shadow mode for two weeks (where it generated responses but humans reviewed and sent them) built confidence across the organization. By launch day, the team had already seen the agent handle hundreds of inquiries correctly.
Measure what matters to the business, not to the technology. The retailer did not care about model accuracy percentages or API latency. They cared about fulfillment speed, customer satisfaction, and staff hours freed up. We structured all reporting around business outcomes.
What This Means for Your Business
If your team spends more than 20% of their time on tasks that follow predictable patterns — data entry, status lookups, routing decisions, standard customer responses — you are a strong candidate for automation.
The technology is mature, the implementation timelines are measured in weeks (not months), and the ROI typically appears within the first quarter.
Talk to our team about a workflow audit for your retail or e-commerce operation →