How to Implement AI in Customer Service: Powering 24/7 Inquiries for Your Business
- manvillechan5
- Jul 28
- 3 min read
As a small business, you likely face a common challenge: customer service and sales inquiries never stop, but hiring a full-time representative might not be feasible. Whether you serve B2B or B2C clients, messages arrive at all hours – sometimes during the day, other times in the middle of the night. While you might not be overwhelmed with inquiries daily, responding promptly and helpfully can quickly consume valuable founder time.
That's precisely why we decided to implement AI in customer service by developing an AI-driven system. This system now manages 100% of our inbound customer service and sales emails. The result? Faster response times, fewer missed sales opportunities, and enhanced customer experiences – all while freeing up our team to concentrate on business growth.
Why We Chose to Implement AI for Customer Service
Implementing customer service with AI allows us to achieve what manual efforts couldn't: respond quickly, accurately, and around the clock. From a customer asking about bringing their own drinks to an event to a corporate client needing assistance with payment options, our AI ensures no question goes unanswered for long. This proactive approach to AI in customer service significantly improves efficiency and customer satisfaction.
How Our AI Customer Service System Works (In Simple Terms)
Even if you're not a developer, we'll walk you through how our system works behind the scenes. When you implement customer services with AI, it typically involves several key stages of interaction every time someone emails us:
New Email = An Alert to Our AI System
When an email arrives, it's automatically forwarded to Zapier, a platform that facilitates communication between applications. Zapier creates a "smart alert" (technically a WebHook) that triggers our AI workflow. We use Gemini 2.5 Flash as our AI agent to power these interactions. (Why did we choose Gemiini over ChatGPT? See this Medium article for our explanation here)
Classifying the Question + Pulling Relevant Info
The first task for our AI (the prompt for the initial API call) is to classify the nature of the email. Is it about a new booking, a payment issue, a cancellation request, or something else?
Once the inquiry type is identified, the system determines if it's from an existing customer. If so, it retrieves their reservation information from our internal database. For example, if someone writes, "I tried to pay but it didn't go through," our AI system knows to pull up their booking and investigate the payment issue further.
3. Analyzing the Message & Drafting a Response
Based on the question type and customer details, we generate a second prompt to understand the context and specifics more deeply. Is the customer attempting to split a payment across two credit cards? Are they requesting a cancellation due to a medical emergency?
From there, our backend logic takes over. We've written extensive rules to check factors such as:
Is the cancellation past the deadline?
Are they eligible for a refund?
Does their email mention keywords like "COVID" or "family emergency" that might qualify for an exception?
Once these rules have been applied, we send all relevant information – context, logic results, and desired message tone – into a third AI prompt, which crafts a well-written, customized email response.
Confidence Score = Automatic or Manual Send Each AI-generated response comes with a confidence level score. If the system is highly confident (typically 90–95% of the time), it sends the email immediately. If it's uncertain, we receive a notification to manually review, refine, and approve the response before it's sent. This hybrid approach ensures accuracy while maximizing automation.
Real Results After 8 Months of Implementing AI in Customer Service
When we first launched this system 8 months ago, we observed approximately 60–70% accuracy in AI-generated responses, meaning nearly one in three emails required manual review. While a good start, it wasn't quite ready for full automation.
Today, after months of refining our prompts, logic rules, and training examples, we consistently achieve a 90–95% success rate. This means most inquiries are handled instantly, without human intervention. Our successful journey illustrates the power of refining your approach when you implement AI in customer service.
And we're not stopping here – our next goal is to bring this same intelligence to live chat support. We were hesitant before, as real-time chats demand instant accuracy, but with our newfound confidence in the system, we're excited to roll it out.
Ready to Implement AI Customer Service for Your Business?
If you're a small or mid-sized business owner curious about how AI can help save time, close sales faster, and improve customer satisfaction, we'd love to connect (yes, with a real human this time 😄).
👉 Visit us at www.VeloLogicStudio.com and let's explore how implementing AI in customer service can work for your business.
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