Gamdom

    Real-Time Negative Tweet Monitoring System – Gamdom

    An AI-powered social listening system that detects high-risk complaints and escalates them instantly.

    Problem Statement

    Gamdom operates in a high-trust, high-sensitivity industry where public complaints about withdrawals, KYC delays, bans, or stuck balances can escalate fast and damage brand reputation. Before automation, Gamdom faced serious challenges: Important negative tweets were being missed, Complaints written politely slipped past basic sentiment tools, There was no single place to monitor brand mentions, Tweets with screenshots weren’t being captured, Alerts came too late, after issues had already spread, Duplicate alerts created noise, while real issues were overlooked. They needed a precision system that could think like a human support analyst, work 24/7, and never miss a serious issue.

    The Solution

    Automation Overview

    We built a fully automated, AI-powered negative tweet monitoring pipeline using n8n + OpenAI + Slack. The system continuously monitors all Gamdom mentions on X (Twitter), filters out noise, identifies serious complaints with high accuracy, extracts images, and escalates only high-risk tweets directly to Gamdom’s support team in real time. This workflow replaces the need for a manual social listening team.

    Continuous Brand Monitoring

    The system tracks all relevant mentions in real time, including: @gamdom, @GamdomHowly, @pnrGamdom, “Gamdom” keyword variations. This ensures no conversation about the brand goes unnoticed.

    Smart since_id Tracking (Zero Duplicates)

    A persistent since_id system ensures the workflow always knows the last processed tweet. No duplicate alerts, No missed tweets, Safe recovery even if the workflow pauses or restarts. This makes the system fail-safe and production-ready.

    High-Precision Data Extraction

    For each tweet, the automation extracts only what matters: Tweet text, Author username and name, Tweet ID and link, Timestamp, Public metrics (likes, replies, impressions), Image URLs and preview links (if present). Everything is prepared as clean, structured JSON for AI analysis.

    AI-Powered Negative Tweet Detection (Core Intelligence)

    This is the heart of the system. We designed a custom LLM prompt that detects real operational complaints, including: Withdrawal failures, Funds stuck (even small amounts), KYC delays or verification issues, Account bans, Support delays, Liquidity issues, Platform errors, Escalation threats (“taking this to Twitter”), Calm, polite complaints, Indirect or mixed-sentiment criticism. The AI ignores promotions, giveaways, jokes, and neutral mentions — and returns a strict JSON output with clear reasons for classification.

    Image & Screenshot Detection

    Using X API expansions, the system extracts: Image URLs, Preview images, Media metadata. This allows the support team to immediately see screenshots or proof attached to complaints — without opening Twitter manually.

    Instant Slack Escalation

    Every high-risk tweet is sent instantly to Gamdom’s dedicated Slack channel with: Tweet text, Author details, Direct tweet link, Reason for negative classification, Images/screenshots (if any), Timestamp, Engagement metrics for severity context. This gives the team a real-time alert feed they can act on immediately.

    Safe State Updates

    After processing each batch, the workflow updates the stored since_id with the newest tweet ID, ensuring the next run starts from the correct position every time.

    Integrations & Connected Systems

    X (Twitter) API v2 – Real-time tweet search and metadata; OpenAI (LLM) – High-precision complaint detection and reasoning; Slack – Instant internal escalation and visibility; n8n – Workflow orchestration, state handling, parsing, validation. All systems work together as a single automated monitoring engine.

    Smart Logic & Reliability

    • Zero-miss negative detection logic
    • Prompt-engineered NLP for polite and indirect complaints
    • JSON-safe AI communication (no parse failures)
    • Duplicate prevention
    • Image extraction support
    • Graceful handling of empty or noisy tweet batches
    • The system behaves like a trained social listening analyst — without fatigue

    Before

    Manual monitoring, delayed reactions, missed complaints, and reputational risk.

    After

    Every serious issue is detected and escalated in real time — with context, clarity, and precision.

    Tools Used

    n8n
    X (Twitter) API v2
    OpenAI (LLM)
    Slack
    Custom prompt engineering
    Persistent state storage

    Our Process

    1

    Discover

    Analyzed missed complaints and failure points in existing monitoring.

    2

    Design

    Built a precision-first AI classification system.

    3

    Build

    Implemented real-time monitoring, parsing, and escalation logic.

    4

    Integrate

    Connected X, AI, and Slack seamlessly.

    5

    Deploy

    Iterated prompts using real missed tweet examples until zero-miss accuracy was achieved.

    Business Impact

    95% faster awareness of customer issues

    Zero missed high-risk complaints

    Strong reputational protection

    No manual social monitoring required

    Support team receives only actionable alerts

    Clear, context-rich insights inside Slack

    System runs 24/7 on autopilot

    "This automation gave Gamdom a real-time reputation protection system powered by AI. By detecting even subtle or polite complaints, extracting visual proof, and escalating instantly to Slack, the platform now stays ahead of public issues instead of reacting late."

    Want a system like this for your business?

    Let’s build it.