How a 5-person company automated 85% of customer service

Suitor started in a garage. Two founders, a stock of rental suits, and a simple idea: renting formalwear should be easy. By 2023, they'd grown into a million-dollar business and appeared on Australia's Shark Tank, landing two sharks.

But growth created a problem. Customer service queries were increasing faster than the team could handle. Most questions were already answered on the website: sizing guides, delivery timelines, return policies. But people kept asking anyway.

With 5 employees and orders coming through at all hours (many after 8pm), the founders were spending their evenings responding to messages instead of growing the business.

What they changed

They added an AI chatbot trained on their existing FAQs and product information. The setup wasn't a massive project. They loaded their existing Q&A content, customised the chat flows for their most common scenarios, and let the system learn from real conversations over time.

The chatbot handles answers to common questions, learns from each interaction, uses natural language (not rigid decision trees), and automatically hands off complex queries to a human agent.

The numbers

| Metric | Before | After | |---|---|---| | Average response time | 3 minutes | 6 seconds | | Queries handled by AI | 0% | 85% | | Queries needing a human | 100% | 24% |

That 6-second response time isn't a typo. The AI reads the question, matches it against its knowledge base, and responds almost instantly. For a customer wondering whether they can return a suit after the weekend, they get their answer before they've finished scrolling the page.

Why 24% still goes to humans

The interesting part isn't the 85% automation rate. It's the deliberate choice to keep humans in the loop for 24% of conversations.

These are typically questions that involve custom situations: a wedding party with unusual sizing requirements, a last-minute order with tight delivery windows, or complaints that need empathy rather than information. The AI recognises when it's out of its depth and escalates.

This matters because trust is the whole game. An AI that tries to answer everything will eventually give a wrong answer to an important question. An AI that says "let me connect you with someone who can help" keeps the customer's trust.

What this tells us about small business AI

Three things stand out:

You don't need a big team to benefit. Suitor has 5 employees. This isn't enterprise AI with a 6-month implementation. It's a small team offloading repetitive work so they can focus on what actually grows the business.

Speed matters more than perfection. A 6-second response is better than a perfect response that takes 3 minutes. For most customer service questions, the answer is straightforward. Getting it to the customer faster is the win.

24/7 coverage without 24/7 staffing. With many orders coming after 8pm, the founders were choosing between personal time and missed sales. The AI removed that tradeoff.

"I don't even have the Tidio app on my phone because I trust it to do its job." — Daniel Reid, Co-founder and CEO of Suitor

Source: Tidio case study

Could this work for your business?

If your team spends hours answering the same questions over and over, this is the most proven starting point for AI. No fancy models, no data science. Just an AI that handles the repetitive 80% so your team can focus on the conversations that actually need a human. See if it makes sense for your business.

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Related reading:

• 9 real AI case studies from small businesses
• Why most chatbots are terrible (and how to fix yours)
• Is AI worth it for a small business?
• My AI customer support service