Aurora: Self-Driving Success in Helpdesk Efficiency

— CHALLENGE

Manual Helpdesk, Fragmented Knowledge, High Standards

Aurora is committed to providing employees with a seamless and efficient support experience, emphasizing prompt issue resolution and hassle-free internal services. But the reality wasn't matching the ambition.

Employees submitted support requests through Slack channels, relying on custom workflows to respond and create tickets. The company had previously evaluated several helpdesk solutions, but found them insufficiently customizable for their workflows and lacking responsive customer support. Processing requests manually through human agents was not viable - it was too slow, too inconsistent, and negatively impacted overall employee experience.

A critical additional challenge was knowledge fragmentation. Aurora's teams relied heavily on Confluence Server as their internal knowledge base, but this information was siloed and not easily accessible at the point of need - within Slack conversations where employees were actually asking for help. Without a way to surface Confluence knowledge in real time, employees either waited for a human agent to look up answers or created tickets for questions that could have been resolved instantly with the right information.

Additionally, as helpdesk automation relies on knowledge base responses, it required vendors and partners to comply with Aurora's stringent security protocols - adding complexity to the evaluation process.

The objective was clear:

• Resolve employee support requests faster with AI-powered automation tailored to Aurora's custom workflows
• Deliver a seamless employee experience with measurably lower average ticket resolution time
• Leverage existing knowledge efficiently from multiple sources — including Confluence Server, past conversations, and internal documentation — as a single trusted knowledge layer
• Provide actionable insights to continuously identify and prioritize automation opportunities

— SOLUTION

AI-Native Automation with Confluence as the Knowledge Layer

Recognizing the need for AI-native automation, Aurora chose Enjo to resolve employee support requests faster - with responses that match the quality and nuance of human interaction. The goal was not just to deflect tickets, but to autonomously resolve requests end-to-end, reducing handoffs and eliminating repetitive work for agents.

Security evaluation and proof of concept

After completing a thorough security evaluation, Aurora engaged in a 3-month proof of concept. Troopr's dedicated customer success team ensured Enjo fulfilled all use cases, and any gaps identified during the POC were addressed promptly. The team also collaborated with Aurora to prepare a detailed rollout plan.

Full rollout across ~100 Slack channels

The rollout was executed swiftly across numerous teams, deploying approximately a hundred Slack channels for request submission, response handling, and triaging. Multiple AI Agents were tailored to address diverse use cases. Leveraging the customizability of emoji actions further enhanced the setup, delivering a tailored experience for both admins and agents. The entire process — encompassing knowledge base indexing from various sources and configuring ticketing behaviors - was completed within two weeks.

Confluence Server integration: turning siloed knowledge into instant answers

As a key part of the deployment, Aurora connected their Confluence Server instance to Enjo as a knowledge source. In several Slack channels, Enjo now serves as a frontline knowledge assistant - employees ask questions and Enjo instantly surfaces accurate, contextual answers grounded in Confluence content.

When the information needed isn't available in the knowledge base, Enjo automatically creates a ticket, ensuring that no request falls through the cracks and the right team is looped in without any manual effort.

This eliminates the friction of switching between tools, reduces dependency on specific team members who "know where things are," and ensures that Confluence - a knowledge base teams have already invested in building - is actively working for employees at the point of need.

— BUSINESS IMPACT

Record-High Satisfaction, Autonomous Resolution at Scale

Employee adoption of Enjo has been exceptional, with nearly 100% of requests directed to the platform. The autonomous resolution rate — measuring the percentage of requests Enjo resolved without human intervention — stands at an impressive 63%. Furthermore, there has been a significant reduction in average ticket resolution time, leading to record-high employee satisfaction ratings.

Results

• 45%+ reduction in average ticket resolution time
• 63%+ autonomous resolution rate - requests resolved without human intervention
• 60% increase in employee satisfaction
• ~100% employee adoption rate across all deployed channels

Why Enjo worked for Aurora

• Resolve issues faster, not just reply faster - Enjo didn't just provide answers - it completed requests end-to-end, eliminating handoffs and reducing back-and-forth.
• One knowledge layer across AI, agents, and self-service - Confluence Server, past conversations, and internal docs all feed into a single trusted source of truth - ensuring consistent, accurate answers everywhere.
• Seamless Slack-native experience - Employees get answers and create tickets without ever leaving Slack - no tool-switching, no friction.
• Customizable to complex workflows - Unlike rigid helpdesk tools, Enjo adapted to Aurora's specific workflows, emoji actions, and multi-channel setup.
• Fast proof of value - From POC to full rollout across ~100 channels in weeks, with measurable outcomes from day one.