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دراسات الحالة
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2026
Social Media AI Agent for Cash4GoldNow
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الخط الزمني
3 weeks
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الخدمات
أتمتة الذكاء الاصطناعي
إدارة علاقات العملاء المخصصة
UK-based gold buying business asked us to build a AI-powered Social Media Comment Moderation Automation for Facebook and Instagram channels. The system combines keyword filtering with an AI moderation assistant (OpenAI / LLM-based) to classify incoming comments by sentiment and intent, then automatically hide, delete, block, or escalate based on rule outcomes. Every action is logged for audit, with daily summary reporting and human-in-the-loop escalation for ambiguous cases.
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نظرة عامة
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التحديات
C4GN's Facebook and Instagram channels are exposed to a high volume of unwanted activity that creates real commercial risk and operational drag. Specifically:
Brand reputation risk — abusive comments, offensive language, and inappropriate content sit publicly under C4GN posts, damaging perception with prospective gold-sellers who are already cautious about who they trust with valuable items.
Competitor diversion — rival gold-buying companies post comments steering C4GN's warm audience to their own services, siphoning leads directly off paid and organic reach.
Spam saturation — automated promotional comments, scams, and irrelevant content bury legitimate customer enquiries in the comment thread, hurting response time and conversion.
Manual moderation overhead — the team currently relies on humans (Paul, Nick) to monitor, classify, and act on comments across both platforms in real time, which doesn't scale, isn't 24/7, and pulls senior staff away from revenue work.
Risk of over-moderation — without a controlled framework, blanket auto-deletion could silence genuine complaints or legitimate criticism, creating regulatory and reputational exposure (especially relevant in the UK financial-adjacent space).
So How Did We Fix This Bottleneck?
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الحلول
Meta Graph API integration — direct connection into Facebook Pages and the linked Instagram Business Account via Meta Business Manager, using a long-lived page token with scoped permissions (read, hide, delete, block) — no personal credential sharing.
Hybrid moderation engine — a deterministic keyword filter (banned phrases, spam patterns, competitor brand mentions provided by C4GN) runs first for speed and explainability; an AI classifier then handles nuance — sentiment, intent, abuse detection, and ambiguous edge cases.
Five-class taxonomy — every comment is sorted into: spam, competitor promotion, abusive, legitimate enquiry, or uncertain. Each class maps to a defined action (hide / delete / alert / escalate).
Human-in-the-loop escalation — legitimate enquiries and uncertain cases trigger Slack webhook or email alerts to Paul and Nick rather than being auto-removed. Genuine complaints are never silently suppressed.
Audit logging + daily report — every classification and action is recorded in a Google Sheet or approved log store, with a daily summary covering volume moderated, items flagged, and accuracy signals.
Phased rollout with safety gates — Phase 1 only auto-hides clear spam/competitor content; auto-blocking of repeat offenders only switches on in Phase 3 once accuracy is validated on live data. Each phase has explicit exit criteria.
Security & compliance baseline — least-privilege scopes, secure credential intake via 1Password or Bitwarden Send, separation of production and test environments, post-go-live access review, and a documented security statement.
UAT-driven validation — five core acceptance test cases (TC-01 to TC-05) covering spam, competitor referrals, genuine enquiries, abuse, and ambiguity, plus pilot KPIs (auto-moderation rate, false-positive rate, classification accuracy, response time on flagged enquiries).
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النتائج
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دراسات الحالة
