Case Studies

Overview

A mid-sized software company approached Inroad with a common scaling challenge. With annual revenue of $50 million and a high-touch support model split into tiers, their support team was becoming a bottleneck for growth. The company's support engineers were juggling multiple responsibilities: managing customer context in their CRM, filing and testing bugs with engineering, and navigating through docs and internal knowledge bases to resolve cases.

Piloting with Inroad

Inroad's approach was to design a pilot that not only addressed immediate pain points but also laid the groundwork for future scalability and integration with the company's existing infrastructure. The journey to AI-powered support started with a pilot program focused on their Bronze support tier. To ensure that AI could seamlessly integrate into the company's support ecosystem, Inroad designed a targeted pilot that emphasized accuracy, responsiveness, and efficiency:

• Custom-trained AI agents specifically designed for software support
• A purpose-built web app for interacting with customers
• Deep integration with existing systems including CRM, bug tracking through Jira, and documentation

The pilot was evaluated using metrics including case resolution accuracy / time, cost of Support labor, and customer satisfaction score. Each of these metrics were benchmarked against the KPI's of the existingBronze tier support team.

Business Impact

Out of the gate, feedback on the AI agent from customers was "overwhelmingly positive." The results were striking and immediate, showing a measurable improvement across several key metrics:

• 95% decrease in average case resolution time
• 90% reduction in labor costs
• Maintained premium pricing with higher satisfaction

Inroad also received the anecdotal feedback that "the agent found an issue in our product documentation that has been there for over 10 years." The combination of rapid case resolution, cost savings, and sustained pricing reinforced Inroad's potential to drive both efficiency and long-term customer satisfaction.

Takeaways

This pilot and subsequent implementation highlighted several key learnings for us that will help inform future AI agent implementations at Inroad:

• AI agents excel at research and communication tasks when properly trained
• With custom-trained AI agents and a purpose-built interface, the solution provides a personalized and intuitive experience that is closely aligned with the company's brand and customer expectations
• This solution connects seamlessly with the company's CRM, bug-tracking, and documentation systems, enabling a smooth flow of information that out-of-the-box solutions often lack

We ultimately showed that support operations can be transformed from cost centers to revenue generators. The company plans to continue working with Inroad to optimize their AI-powered support system and explore additional opportunities for automation and efficiency improvements across their customer service operations.