This is not just a story about 'teaching AI to reply to reviews'. It's about creating a bespoke ticketing system for a support team, complete with rules, multiple models (ChatGPT and Gemini) and quality control by real experts.
Why feedback matters
User feedback can fuel entire industries. The successes and failures of gaming, foodtech, marketplaces, SaaS, e-commerce and delivery are built on what customers say about the business: ratings, reviews, questions, complaints about poor service and words of gratitude. Every piece of feedback is a signal showing where your product is heading.
And those signals are multiplying. According to HubSpot's Annual State of Customer Service Report, 75% of support professionals faced the highest volume of enquiries in the history of customer service in 2025.
When the flow of requests is manageable, there’s nothing to worry about. However, launch a flagship product or allow errors to creep into your order process and the number of reviews will increase faster than your support team can handle.
In reality, every customer deserves a response, whether they are satisfied or frustrated. The cost of silence is simply too high:
- 75% of customers are willing to spend more with companies that deliver excellent service (Zendesk Benchmark Data);
- 43% of customers say that one bad service experience is enough to put them off buying again (Salesforce Data Research);
- Negative customer experiences put $3.7 trillion of global sales at risk every year. (Qualtrics XM Consumer Study).
We provide more reasons for systematic feedback management in this guide
Ticketing systems and help desks help companies to bring order to chaos. These digital platforms collect requests from all channels, convert them into tickets containing detailed information, assign them to agents, track their status and generate analytics.
However, businesses still have critical questions:
- Where can we find a system that collects reviews from app stores, emails, and other sources, and instantly converts them into tickets?
- Is there a tool that combines analytics and ticket management without forcing us to use multiple platforms?
- Why must we choose between automation and feedback analytics when we clearly need both?
For our client, the publisher Malpa Games, we designed a custom ticketing system that closed both gaps at once: review aggregation and analytics. The product proved its value right after the first release. Then we took it further: could AI reduce the repetitive workload of the support team and boost productivity?
Spoiler: it could — and it did.
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AI and Ticketing Systems: From Review Processing to Product Analytics
The effectiveness of customer support is most often measured using three key customer experience (CX) metrics. According to research from 2025, business priorities are distributed as follows:
- CSAT (Customer Satisfaction Score): named the top priority by 31% of respondents;
- Customer retention, which is directly tied to revenue growth, was also ranked as a top metric by 31% of companies;
- Response time, the speed at which a customer receives a reply, was cited by 29% of surveyed professionals (HubSpot’s Annual State of Customer Service Report)
In the gaming industry, the volume of reviews is consistently massive and highly diverse: some are short “⭐️⭐️⭐️⭐️⭐️ Everything’s great!” notes, others are detailed negative complaints, and many are direct questions. Handling such a mix manually consumes enormous time, while businesses must prioritize two criteria above all: response speed and consistency.
These metrics directly affect CX outcomes. CSAT scores decrease if players wait too long for a reply, and retention rates fall when responses are inconsistent or contradictory. That’s why automation is no longer optional. Without it, managing this scale is impossible, and the risk of losing trust is too high. Customers support this shift themselves: 46% say they are open to AI-powered support if it can solve their problem (Salesforce Data Research).
Malpa Games integrated AI into their ticketing system to solve four practical challenges:
- Detecting the sentiment of a review (negative, positive, or neutral). This allows for quick prioritization: critical tickets are sent to manual review first, while positive ones are closed automatically.
- Identify the cause of negative feedback (e.g., “too many ads” or “crashes on Android 14”). This helps not only support but also product teams, by highlighting systemic issues and irritation points that require fixes.
- Assign tags to systematize reviews. This simplifies analytics, allowing teams to track trends and generate reports without manual labeling.
- Generate a reply by drafting a response or selecting a ready-made template for standard cases. This frees specialists to focus on complex inquiries that require empathy and context.
To test this approach, we integrated two models, ChatGPT and Gemini, directly into the system. We aimed to compare their classification accuracy and response style in real-world scenarios. Ultimately, we plan to keep only one model, but for now, we’re experimenting to see which one provides more value to the support team.
Malpa Games. Customer feedback management software for a mobile games publisher
More features of the Malpa Games ticketing system are covered in the full case study
How Ticket Automation with AI Works
We started with the simplest, but important scenarios. Today, AI closes:
- Empty tickets where a user leaves only a star rating and the record contains technical metadata (e.g., device, operating system, and app version);
- Short positive reviews with no questions or requests.
The model analyzes each item, checks it against predefined parameters, and automatically selects an appropriate reply from a prepared directory of automated replies.
We keep several variants for every ticket group, and one is chosen at random so that the conversation doesn’t feel robotic.
For example, a player leaves: “⭐ ️ ⭐ ️ ⭐ ️ ⭐ ️ ⭐ ️ Everything’s great!”
AI detects: language – English; length – under 50 characters; rating – 5; no question.
The system then replies using one of the templates (delivered in the user’s language), e.g.:
- Thanks for the review! Glad you’re enjoying the game 😊
- Thanks for the high rating! Enjoy!
- Thanks for playing – it really motivates us!
The ticket is closed without human involvement; the player gets a fast, personable response.
How to implement Al in your business - A Complete Guide
More ideas for integrating AI into business workflows are covered in this guide
The step-by-step flow looks like this:
- The ticket enters the system. If the ticket contains only a rating, an automatic reply is sent right away.
- If the ticket has a positive rating and text, the AI identifies the language and context. Then, both models generate ready-to-use reply options.
- If needed, the reply can be regenerated or translated into another language. The manager copies the final version, sends it to the user, and closes the ticket.
Automation Rules
Rather than sending all reviews blindly into the model, we made the AI's work transparent and controllable through an auto-reply directory.
This allows teams to precisely define how the AI handles tickets:
- By language – auto-replies are only possible for supported languages;
- By text length – short reviews can be closed with a template, while longer ones are sent for analysis;
- By rating – positive reviews can be automated, while negative ones require manual review.
Rules can also be assigned selectively for specific company projects or products.
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Integration Results
In its first year, the AI-powered Malpa Games ticketing system processed 10,000 "empty" tickets (cases in which a user provided only a rating) and an additional 8,000 brief standard reviews. That’s nearly 40% of the entire flow, or 18,000 tickets out of 47,000 total.
The time savings are substantial. On average, a support manager spends about two minutes handling a simple ticket: opening it, reading it, assigning a tag, selecting a reply, and sending it. AI eliminated the need to do this manually 18,000 times. In total, that equals 600 working hours – nearly four months of a full-time support manager's work.
Three Lessons from Automating Support with AI
The Malpa Games case clearly demonstrates that basic automation can improve response speed and strengthen the customer experience:
- Simple cases have a measurable impact. Automatically closing empty ratings and short reviews saves hundreds of hours of routine work and stabilizes response time metrics.
- Classification creates product value. Detecting sentiment and root causes of negative feedback provides actionable insights for product teams and helps them prioritize bug fixes.
- AI amplifies, rather than replaces, the team. Automatic draft replies reduce response times while allowing managers to focus on complex issues where empathy and context matter.
Looking ahead, scenarios can become more advanced, such as forecasting the support workload, automatically routing tickets to agents, and dynamically generating answers from a knowledge base or user behavior data. At that stage, AI becomes a strategic tool for managing customer support at scale, not just a helper with routine tasks.
