For high-growth companies like Zipify, scaling customer support operations without sacrificing quality or efficiency is a critical challenge. But what if your support agents had an AI-powered co-pilot, guiding them to the right answers, automating knowledge creation, and transforming every interaction into an opportunity for improvement? This is exactly what Master of Code Global helped Zipify to implement.
Discover how our custom developed solution streamlined their support workflows and unlocked valuable insights to drive continuous improvement.
Zipify’s support team was consistently handling large volumes of customer inquiries, many of which were repetitive or required detailed, context-specific answers. In addition, the team lacked a centralized way to analyze conversation trends, agent performance, or evolving customer needs. Relying on general-purpose AI solutions was not sufficient: they didn’t fully capture the unique processes, documentation, and decision-making patterns the support team followed. Therefore, a more specialized tool was needed—one that could not only streamline daily workflows but also provide deeper insights through analytics.
MOC Global took a bespoke approach, designing and developing a cutting-edge AI solution tailored to Zipify’s specific challenges and objectives. This project comprised two key components: an intelligent virtual assistant for agents and a comprehensive analytical dashboard. This way we helped Zipify scale its support operations without sacrificing quality or efficiency.
This custom-built digital helper serves as an invaluable resource for Zipify’s support team, providing real-time assistance and knowledge retrieval within their existing workflow.
Contextual Answers & Summaries: No more endless searching through documentation. The assistant rapidly analyzes past conversations and a curated help center to deliver concise, accurate answers to agent queries, ensuring swift resolution and consistent customer experiences.
AI-Driven Article Creation: This innovative feature automatically transforms weekly support conversations into valuable help articles, published directly to an internal knowledge center. This creates a constantly growing library of real-world solutions, reducing repetitive questions and equipping agents with readily available information.
Seamless Intercom Integration: No disruptive transitions or cumbersome workflows. The virtual assistant is embedded directly into Zipify’s Intercom interface, giving agents seamless access to critical insights without interrupting their natural flow.
Complementing the virtual assistant, MOC Global built a robust Analytical Dashboard to provide Zipify with unparalleled visibility into their support operations.
Performance Tracking & Goal Setting: This dashboard tracks crucial metrics such as average response time, issue resolution rates, and agent workload, enabling Zipify to set measurable goals, pinpoint areas for improvement, and optimize overall team performance.
AI-Powered Quality & Sentiment Analysis: Going beyond basic metrics, the dashboard utilizes AI to evaluate agent responses for clarity, politeness, and relevance while simultaneously gauging customer sentiment. This allows Zipify to identify emerging trends, address potential challenges, and ensure consistently positive customer interactions.
Leaderboard & Agent Recognition: A bit of friendly competition can be a powerful motivator. The dashboard incorporates a leaderboard that ranks agents based on performance, fostering a culture of excellence and recognizing top performers who consistently deliver exceptional support.
We designed an engaging, guided conversational flow for the virtual assistant using robust natural language processing (NLP). The bot connects to a specialized vector database of articles and conversation logs to provide relevant answers. Meanwhile, the Analytical Dashboard pulls live data from the AI bot and Intercom, turning complex support interactions into actionable insights. What were the steps?
1. Integrating Knowledge
2. Crafting the Dashboard
3. User Feedback & Iteration