How an AI Agent Cut Ticket Processing Time by 60% at a Service Company: A Case Study from Ukraine

Starting point: company type, ticket volume, typical problems without automation

This case study looks at a Ukrainian service company that repairs household appliances in and around Kyiv. Their contact center was receiving 3,200–3,500 tickets a month (around 90% through messaging apps, the rest through the website and phone). Typical problems without automation:

• Agents spent 3–5 minutes per ticket (over 6 minutes during peak hours).
• Frequent response delays — in the evenings, up to 20% of tickets waited more than 30 minutes for a reply.
• A high rate of lost potential customers due to slow response times.
• The cost of manual processing kept climbing along with ticket volume.

The load on agents became critical during seasonal peaks. The company considered hiring more staff, but that meant added costs and training headaches. That's when we proposed testing an AI agent to automate the initial handling of inquiries.

How we implemented the AI agent: integrations, scenarios, channel selection

The first task was figuring out which tasks the AI agent could take on without hurting service quality. We analyzed 2,000 historical conversations: 72% of inquiries were routine, repeatable scenarios (booking a diagnostic visit, confirming pricing, address, business hours).

Implementation stages:

1. Channel selection: we started with Telegram, Viber, and the website (live chat). Phone calls stayed with human agents for complex cases.
2. CRM integration: the AI agent was connected to the company's internal AI-powered CRM system — to check customer history, automatically create tickets, and pass information to technicians.
3. Building scenarios: we set up 15+ conversation branches, from confirming the appliance model to picking a convenient visit time.
4. Training on real examples: we used the company's own dataset — 5,000+ conversations — to fine-tune the agent's responses.
5. Human oversight: if the AI agent isn't confident in its answer, or the customer is unhappy, the conversation is automatically handed off to a human agent.

The results: time, savings, reduced workload

Within the first month after launching the AI agent, the company saw:

• Average ticket processing time dropped from 4.2 to 1.7 minutes (a 60% cut).
• 80% of tickets are now handled without a human agent — staff only step in for complex or conflict-prone situations.
• Evening response time (6–10 PM) improved from 11 minutes to 1.4 minutes — wait-related lost customers practically disappeared.
• Monthly savings equivalent to 2.1 full-time agent positions (roughly UAH 36,000/month).
• Customer satisfaction held steady (NPS stayed stable at 8.4 out of 10).

For more on similar projects, see our AI Agents and Chatbots section.

Implementation cost and payback

Cost of implementing the AI agent for this company (2024):

• Initial setup and integrations: UAH 85,000 (one-time).
• Monthly support and scenario refinement: UAH 11,000.
• IT infrastructure (servers, API, security): UAH 4,200/month.

Payback:

• Combined savings on agent salaries: roughly UAH 36,000/month.
• Full payback period: 3.1 months (based on real client data).

Compared to classic chatbots, the AI agent showed greater flexibility (it understands "atypical" questions and adapts to the customer's communication style) and scaled more easily to new channels.

Takeaways for other companies

1. Start by analyzing your inquiries. Figure out which scenarios can genuinely be automated — not everything is a good fit for AI.
2. Don't skip CRM integration. Without access to customer history and up-to-date information, the agent ends up "half-blind."
3. Plan for human intervention. Don't try to eliminate agents entirely — you still need them for complex cases and quality control.
4. Measure the impact in numbers. Track time spent before and after, calculate savings, and monitor customer satisfaction.
5. Be ready to iterate. Real-world cases require ongoing scenario tuning after launch.

For more practical implementation examples, see our MaxICo Labs Case Studies section. If you're interested in an overview of automation possibilities, check out AI for Business (overview).