In 2025, over 70% of online game and social platform developers report a sharp increase in complaints related to toxic user behavior (Statista, Unity Gaming Report). Game chats, in-game communication, and user comments are increasingly becoming sources of aggression, spam, and unsolicited advertising. This is not just a UX issue — it’s a direct threat to retention and monetization.

According to Newzoo, 58% of players leave multiplayer products after just one negative communication experience. And in platforms with user-generated content (UGC), up to 80% of toxicity incidents are recorded in text-based interactions. With millions of daily messages, manual moderation becomes infeasible.

The solution lies in intelligent automation. Kolas.ai is an API-based service that can instantly classify any message as insult, spam, commercial, or neutral. As a software architect, I will walk you through a real case where Kolas.ai was integrated into a mobile MMO game — and demonstrate the measurable business outcomes it delivered.

1. The Problem: Toxic Chats as a Churn Driver

In multiplayer games, chat is a central feature. It can either drive community engagement — or destroy it. Common issues include:

  • Insult: offensive, aggressive, or discriminatory messages.
  • Spam: irrelevant, repetitive, or low-value content.
  • Commercial content: promotions for in-game currencies, account sales, etc.
  • Flooding: high-volume or overly frequent messages.

When these problems are left unchecked, users disengage from communication, disable the chat, or abandon the product entirely.

2. The Solution: Integrating Kolas.ai into Your Stack

Kolas.ai offers an API-first solution that developers can connect directly into their message processing logic.

Integration Modes:

  • Synchronous Mode (REST API) — for real-time processing before message delivery.
  • Asynchronous Mode (Webhooks + Task Queue) — ideal for background moderation and analytics of message history.

Sample Architecture:

  • The chat server (e.g., built with Node.js or Go) intercepts incoming messages.
  • It sends a request to Kolas.ai with the message payload.
  • The response might look like: { "label": "commercial", "score": 0.91 }.
  • Based on the result:
    • The message is displayed or blocked;
    • A warning is issued to the user;
    • Incidents are flagged in a moderation dashboard.

We also implemented a local LRU cache for repeated phrases, which reduced API calls by 22%.

3. Why Building Your Own ML Filter Isn’t Worth It

The client initially considered building an in-house solution. However, it would require:

  • Collecting and labeling a dataset of 50,000+ examples;
  • Setting up infrastructure for training and hosting models (GPU, CI/CD pipelines);
  • Monitoring quality metrics like precision, recall, and F1-score;
  • Ongoing support to prevent model drift and accuracy degradation.

The internal cost estimate was 3–5 months of work by two engineers and $15,000+ in infrastructure and labor.

With Kolas.ai, the same system was up and running in just 3 days with:

  • Classification accuracy above 92% (F1-score);
  • No need for internal NLP or ML teams;
  • Scalable architecture ready for production loads.

4. Real-World Results

Product: Mobile MMO game with 60,000 DAU
Message Volume: ~270,000 per day

Post-integration Results with Kolas.ai:

  • Toxicity complaints dropped by 73% in the first month;
  • Chat participation rose by 25% — users were more engaged;
  • Manual moderation time was cut by 80%;
  • User bans decreased by 19% due to early warning logic;
  • Day-7 retention improved by 12%.

All metrics were validated through built-in analytics and A/B testing.

5. Scalability and Customization

Kolas.ai is built to scale and can process up to 5 million messages per day per client.

Additional Features:

  • Custom labels — e.g., “political propaganda”, “support requests”, “religious discourse”.
  • Multilingual support — English, Russian. If necessary, we can add any language upon request.
  • Performance benchmarks:
    • Supports up to 10,000 requests per minute without degradation;
    • Guaranteed uptime: 99.98%;
    • Distributed architecture with automatic failover.

Roadmap for 2025–2026 includes emoji sentiment analysis, sarcasm detection, and LLM-powered semantic labeling.

Conclusion: A Smart, Strategic Choice for Growth

Toxic content, spam, and unsolicited ads are inherent risks for any product that allows user-generated content. Ignoring this risk is a strategic failure. Choosing a tool like Kolas.ai is an investment in retention, engagement, and brand safety.

Just like choosing a credit card requires a careful review of rates and reliability, selecting a moderation engine demands consideration of quality, latency, scalability, and vendor trust. Kolas.ai checks all those boxes.

Create your free account at app.kolas.ai to:

  • Get 5,000 free classifications per month,
  • Access complete API documentation,
  • Explore a live moderation demo,
  • View detailed analytics on flagged messages.

Start protecting your product today — with minimal engineering effort and maximum effect.

Kolas.ai — your intelligent filter for toxicity, spam, and unwanted content.