Overview

As a leading e-commerce accelerator in the luxury sector, our client has helped over 50 high-end brands across fashion, design, and luxury goods rapidly scale their online presence. With an ever-increasing volume of customer support requests, from order tracking to complex logistics, their existing workflow became inefficient. Long resolution times and overwhelmed agents became a bottleneck as the business grew.Solution
Stackdrop built a centralized Retool-powered dashboard and layered in AI to automate categorization, suggest responses, and translate inquiries instantly.

Our role: A Transformative Partner

At Stackdrop, we specialize in low-code, AI-powered solutions that improve team efficiency and scalability. Using Retool as the backbone, we partnered with this e-commerce leader to centralize their ticketing system and integrate AI-driven automation. Our focus was on speed, precision, and empowering them to implement meaningful changes without overburdening their internal resources.

The Challenge | What the Company Struggled With Before Stackdrop

🕒 Time to First Response Was Too Long

Challenge Agents had to open up to six different tools (Zendesk, Kibo, Qapla, and internal databases) just to resolve a single ticket.

Impact This resulted in delayed first responses, as agents spent more time gathering data than actually addressing customer issues.

Opportunity Centralizing data into one platform would immediately reduce response time, making the support experience faster and more efficient.

🔄 Lack of Automation & Manual Workload

Challenge The support system lacked automation. Agents were tasked with manually categorizing tickets and drafting responses for common inquiries.

Impact This led to inconsistent response times and high manual workload for agents, preventing them from focusing on more complex issues.

Opportunity Introducing AI-powered categorization and pre-filled responses would reduce the manual work, allowing agents to focus on high-priority tickets and respond faster to customers.

📈 Scalability & Complexity of Customer Requests

Challenge Our client's customer support team struggled with the variety and complexity of incoming requests, including frequent returns and complex policy queries.

Impact The absence of an automated decision-making system meant agents had to check each ticket against policies individually, adding extra time to the resolution process.

Opportunity AI-driven decision trees could be implemented to automatically handle routine inquiries (like returns or order tracking), enabling agents to focus on more intricate cases.

Implementation process

Rather than jumping straight into building a solution, we started with a structured discovery phase to understand the pain points and constraints of the support system.

Our discovery phase allowed us to pinpoint key pain points, identify the most time-consuming tasks, and design a solution that would streamline the workflow and maximize efficiency.

Steps taken

A structured path from insight to implementation

Step 1: Discovery Phase with Stakeholders

We engaged with the support team, operations managers, and IT leads to map out their workflow and pain points. This process helped us understand:

• Where agents were losing the most time in ticket resolution.

• Which types of tickets were most repetitive and could be automated.

• How fragmented data was impacting efficiency and response accuracy.

Step 2: Understanding the Data Model and Fragmented Platforms

The client's ticketing workflow relied on multiple systems that didn't communicate well with each other. We conducted a deep dive into:

• How data was stored across Zendesk, Kibo, Qapla, and internal databases.

• What information agents needed most frequently to resolve tickets faster.

• How we could design a single source of truth for ticket resolution.

Step 3: Ideating and Defining the MVP

Once we identified the key bottlenecks, we designed a minimum viable product (MVP) that could deliver immediate impact while setting the foundation for AI-driven automation. The MVP included:

• A centralized ticketing system that consolidates all necessary data into a single interface.

• An automated categorization system to reduce manual sorting of tickets.

• A scalable infrastructure that would allow AI integration in future phases.

→ With this structured approach, we ensured that our solution wasn't just another tool but a strategic shift in how our e-commerce leader handled support operations.

The Solution: A Two-Phase Approach

Phase 1: Centralized Ticketing System: A Game Changer

To eliminate inefficiencies, Stackdrop built a centralized ticketing system using Retool, allowing agents to:

• Instantly access customer order details without switching between platforms.

• Reduce manual search time, allowing them to resolve tickets faster.

• Rely on a single dashboard that integrates Zendesk, Kibo, Qapla, and internal databases.

This system handled 20,000 tickets efficiently, reducing ticket resolution times and improving overall workflow.

Phase 2: AI-Powered Automation: A Major Shift

While the centralized system was a massive improvement, the company wanted to go a step further. Many customer requests followed predictable patterns, order tracking, refunds, address changes, yet agents were still manually handling them. The next phase was integrating AI-powered automation to categorize tickets, extract order details, and generate pre-filled responses for agents.

Early testing showed promising results:

• AI learned to categorize 80% of incoming support requests with high accuracy.

• Instead of manually crafting replies, agents could approve AI-generated responses with one click.

• Multilingual support allowed AI to translate responses based on the customer's language.

• More complex cases were flagged for human review, ensuring that AI only assisted where appropriate.

Results

Measurable Impact and Expected Outcomes

By implementing a centralized system first, then layering AI-powered automation on top, our client was able to streamline its support operations significantly.

• Average Handling Time (AHT) reduced from 3-4 minutes to 30 seconds per ticket

• AI now automates 80% of incoming tickets, drastically reducing repetitive workloads for agents.

• Multilingual AI capabilities allow our client to provide consistent, fast responses globally.

• The system ensures that only complex cases require human intervention, improving efficiency.

This transformation not only optimized daily support operations but also positioned them for future scalability. As AI continues to evolve, the potential for further automation and expansion into other areas—such as sales inquiries, onboarding, or internal service requests—remains strong.

From a fragmented, manual-heavy support process to a streamlined, AI-powered workflow, the luxury retailer has embraced innovation to future-proof its operations.

Want to streamline your business? Talk to a Stackdrop expert today!