Case Study: Automating saves 160 hours and $4,800 per month

Client Overview

A small company receives about 100 customer requests for the same task every day and was spending significant staff time answering one of the simplest and most repetitive tasks in their inbox.

The Challenge

Every day, the client's customer support team manually handled about a hundred requests. Each one followed the same basic pattern:

• A customer emailed asking for help
• The support person had to look up the requestor in the system
• The support person had to then open a ticket
• The support person had to carry out the task
• The support person then had to respond and close out the request

Individually, each request took a minimal amount of time. But multiplied across so many tickets a day, every day, it added up to a major drain on the support team's time, valuable time that could otherwise go toward higher-value work like supporting more complex customer needs.

The Solution

We built a lightweight AI automation that intercepts incoming email requests, identifies the request type, requestor and customer details from the message, processes the request real-time and sends an accurate reply automatically, no agent involvement required.

How it Works:

1. Detect: The automation monitors incoming support channels and flags messages matching request type.
2. Retrieve: It pulls the relevant information and processes in real time.
3. Respond: It generates and sends a automated reply with the completion status of the request.
4. Escalate when needed: If the request fails, it's automatically routed to a human instead of getting a generic reply.

The system was designed to handle the routine for 93% of cases automatically, while keeping people in the loop for the ones that fail to process.

Results

By removing a high-volume, low-complexity task from the team's plate, the client effectively reclaimed the equivalent of a full-time employee's monthly hours, without adding headcount or increasing support costs.

Why it Worked

This automation succeeded because the task was:

• Repetitive: the same request happening a bunch of times a day
• Rule-based: the answer depended on retrievable data, not subjective judgment
• Well-bounded: clear criteria for when to automate versus when to escalate to a person

Rather than trying to automate every kind of customer interaction, the project focused on the highest-frequency, lowest-complexity request first; delivering fast, measurable ROI with minimal risk.

Client Takeaway

"We didn't need a massive overhaul, we needed our team's time back. This automation handled exactly the kind of request that was eating our day without us even realizing it."

Interested in identifying similar automation opportunities in your own support workflow? Let's talk about where your team is spending time on repetitive requests that could be handled automatically.