How an AI Assistant Handled 10,000+ Tickets Per Day
KupiCode was processing thousands of customer requests daily — payment issues, order statuses, refunds. The support queue kept growing, and average response time exceeded three days. We deployed an AI assistant that solved the problem in one month.
The Problem: Support That Doesn't Scale
KupiCode is a major digital services platform with high transaction volumes. Every day, more than 10,000 orders flowed through the system, and a significant portion of customers reached out to support with the same questions: payment didn't arrive, order stuck in an unknown status, refund needed.
Support operated in the traditional model — human agents manually handled each request, checked every order in the backend system, contacted payment providers, and replied. With this volume of tickets, average response time hit three full days. Customers churned. Negative reviews piled up. Agents burned out.
The international nature of the service made things worse: requests came in Russian, English, Spanish, Portuguese, and other languages. Maintaining a multilingual support team was expensive and still couldn't cover peak loads.
Every day without a response cost KupiCode not just a lost customer, but chargebacks — forced refunds initiated through banks. This drove up payment processing fees and damaged the platform's reputation with payment providers.
The Brief: Automate Without Losing Quality
The KupiCode team came to us with clear requirements:
- Automate common request handling — order statuses, payment issues, refunds.
- Integrate with the backend system — the AI must not just reply with templates but actually check the status of each specific order in the database.
- Make refund decisions autonomously — if an order is confirmed unfulfilled, the assistant initiates a refund without human intervention.
- Work everywhere — on the website, Telegram, WhatsApp, and other messengers.
- Support multiple languages — detect the customer's language and respond in it.
The client emphasized one critical point: the assistant must handle objections — not just serve up reference answers, but engage in a real dialogue, explain the situation, and offer solutions. A customer whose payment didn't go through is already frustrated — a dry bot response only makes things worse.
The Solution: An AI Assistant With Full Access to Order Data
We designed and deployed an intelligent assistant that became the full-fledged first line of KupiCode's support. The architecture included several key components.
Deep Backend Integration
The assistant connects directly to KupiCode's platform API. With every incoming request, it queries order data in real time: current status, transaction history, payment verification results, digital product delivery data. This isn't a template-based chatbot — it's a system that sees the same information a human agent does.
Automated Refund Processing
When the assistant determines that an order was not fulfilled and payment is confirmed, it autonomously initiates a refund through the payment system API. The algorithm checks multiple conditions: order status in the backend, time since payment, whether the product was delivered, and the customer's previous request history. Only when all criteria match does the refund execute automatically. Edge cases are escalated to a human agent with full conversation context.
Intelligent Objection Handling
We trained the assistant to work like a seasoned support agent. When a customer writes "Where is my order?! I paid three hours ago!" — the AI doesn't respond with "Your request has been received, please wait." It checks the specific order, explains the current status in plain language, and suggests concrete next steps. If the order is genuinely delayed — it apologizes, explains the reason, and offers a solution, up to an instant refund.
The model was trained on real conversations from KupiCode's best agents. We analyzed hundreds of dialogues, identified patterns of successful conflict resolution, and built them into the prompt system. The result — responses that customers can't distinguish from a human operator.
Multi-Channel Deployment
The assistant was simultaneously deployed as a website widget on KupiCode, connected to a Telegram bot, integrated with WhatsApp Business API and other messengers. One engine, one knowledge base, one logic — but adapted to each channel's format. On Telegram, the assistant uses inline buttons for quick action selection. On the website — a full chat interface with the ability to attach screenshots and receipts.
Multilingual Support
The system automatically detects the language of each request and conducts the entire conversation in that language. At launch, Russian, English, Spanish, Portuguese, German, and French were supported. Response quality doesn't drop across languages: tone, politeness level, and objection-handling capability remain identical regardless of language. For human agents, this would have been impossible without a staff of multilingual specialists.
Implementation Process
WEEK 1–2
Audit & Architecture
Analysis of existing request flows, classification of ticket types, decision logic design, defining automation boundaries and escalation rules.
WEEK 3–4
Integration & Training
Connecting to the backend and payment system APIs. Training the model on real agent conversations. Configuring the prompt system for objection handling across all supported languages.
WEEK 5
Pilot Launch
AI and human agents working in parallel. Every assistant response was reviewed by a supervisor. Pilot accuracy reached 94% — higher than the average for newly hired agents.
WEEK 6–8
Full Launch & Optimization
AI switched to primary support line. Human agents handle escalated cases only. Continuous improvement based on feedback and dialogue analysis.
Results: Numbers That Speak for Themselves
Within the first month of launch, over 60% of support staff were reallocated to other tasks — product development, partner relations, quality analytics. Not laid off — redistributed to positions where their expertise delivers more value to the business.
The AI assistant handles over 10,000 requests per day in fully automated mode. Customer satisfaction didn't just hold steady — it increased, thanks to instant responses and real problem resolution instead of "Your request has been forwarded to a specialist."
Chargebacks dropped by 40%: when a customer receives a refund in two minutes instead of three days, there's no reason to dispute through the bank.
At 10,000 requests per day, even one minute saved per ticket equals 167 person-hours daily. In a month, KupiCode's AI assistant saves the equivalent of 600+ full-time agents on a standard eight-hour schedule.
Technology Stack
The project was built using a combination of proprietary solutions and proven tools:
The assistant runs on a large language model with a custom prompt system, augmented by a RAG architecture — KupiCode's knowledge base that updates in real time. An intake classifier identifies the request type and routes it to the appropriate handling scenario. This allows combining the flexibility of natural language with the rigid business logic required for refunds and verifications.
Key Takeaways
The KupiCode case demonstrates several principles we've refined across 400+ automation projects:
An AI assistant is not a chatbot. The difference is fundamental. A chatbot runs on scripts and breaks on non-standard phrasing. An AI assistant understands context, accesses real data, makes decisions, and holds a meaningful conversation. That's why customers accept it as legitimate support.
Data integration is non-negotiable. Without backend access, an assistant is just a fancy FAQ. Real value emerges when the AI can check a specific order, see a specific payment, and make a specific decision.
People don't disappear — they grow. Automating routine work doesn't mean layoffs. KupiCode's agents moved to higher-value tasks: feedback analysis, model training, VIP client management, partnership development.
Response speed changes everything. In a world where customers expect instant answers, three days of waiting is a death sentence. Going from 72 hours to 2 minutes didn't just move a metric — it transformed how customers feel about the service.
We expected AI to close some tickets. We didn't expect it to transform our entire approach to customer service. Support is no longer a cost center — it's a competitive advantage.
— KupiCode Team
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