Alphazed built a fully automated AI marketing pipeline that discovers trending Arabic education topics on YouTube, scores them for relevance, generates video scripts in Arabic, creates images with DALL-E, synthesizes voiceovers with ElevenLabs, composes videos with FFmpeg, runs kids-safety compliance checks, and publishes to YouTube — all with a human approval gate via Slack before anything goes live.
The 13-Step Pipeline
Step 1: Discover Trends
└─ YouTubeAPI: Fetch trending videos in Arabic education category
Search queries: "تعليم", "أطفال", "تعلم", "عربي"
Extract: title, views, velocity (views per day), comments, subscriber engagement
Step 2: Score Trends
└─ TrendScorer: Weighted formula
Score = (views × 0.35) + (velocity × 0.30) + (topic_fit × 0.20) + (region × 0.10) + (safety × 0.05)
Threshold: Only trends scoring >75 proceed
Step 3: Ideate
└─ ContentIdeator: Generate video concept
Input: Trending topic (e.g., "تحفيز الأطفال على تعلم العربية")
Output: Video concept, target age, learning objective
Step 4: Script Generation
└─ GPT-4o: Generate Arabic video script
Prompt: "Create a 2-minute YouTube shorts script about [topic] for [age] children in Arabic"
Output: Scene-by-scene script with narration
Step 5: Hook Variants
└─ HookGenerator: Create 3 different opening hooks
Variant 1: Story-based opening
Variant 2: Question-based opening
Variant 3: Challenge-based opening
Later, A/B test which hook gets highest CTR
Step 6: Storyboard
└─ StoryboardGenerator: Create visual sequence
Input: Script
Output: Shot-by-shot breakdown (20-30 shots for 2-minute video)
Step 7: Image Generation
└─ DALL-E: Generate visuals for each shot
Prompt: "Child learning Arabic letter ب in a colorful classroom"
Output: 20-30 images, style-matched
Step 8: Voiceover Synthesis
└─ ElevenLabs: Generate Arabic narration
Voice: Female voice, child-friendly, clear articulation
Language: Arabic (Saudi dialect for broad appeal)
Output: MP3 audio, speech marks for lip-sync reference
Step 9: Video Composition
└─ FFmpeg: Assemble video
Input: Images (step 7) + audio (step 8) + background music
Output: MP4 video, 1080p, optimized for YouTube Shorts
Step 10: Compliance Check
└─ KidsSafetyChecker: LLM scan for inappropriate content
Check: No violence, no inappropriate language, no third-party IP
Output: Pass/Fail + notes
Step 11: Slack Approval Gate
└─ SlackBot: Post video preview + metadata
Team reviews: thumbnail, title, description, transcript
Approval options: ✓ Publish | 🔄 Revise | ✗ Reject
Step 12: Publish
└─ YouTubeAPI: Upload to Alphazed channel
Title, description, tags, thumbnail
Visibility: Public
Step 13: Analytics Sync
└─ YouTubeAnalytics: Track performance
Metrics: Views, CTR, avg. watch duration, shares
Feedback: Use metrics to improve future scripts
Trend Scoring Algorithm (Step 2)
The Formula
def score_trend(trend_data):
"""
trend_data = {
'views': 500000,
'days_since_upload': 7,
'topic': 'تعليم الأطفال العربية',
'language': 'ar',
'age_group': '5-12',
'video_category': 'education'
}
"""
# Component 1: Raw popularity (views)
popularity_score = min(trend_data['views'] / 1_000_000, 1.0) * 100 # Capped at 100
# Max out at 1M views = 100 points
# Component 2: Velocity (growth rate)
velocity = trend_data['views'] / trend_data['days_since_upload']
velocity_score = min(velocity / 100_000, 1.0) * 100 # Capped at 100
# 100k views/day = 100 points
# Component 3: Topic fit
relevant_keywords = ['عربية', 'قرآن', 'أطفال', 'تعليم', 'لغة']
keyword_matches = sum(1 for kw in relevant_keywords if kw in trend_data['topic'])
topic_fit_score = (keyword_matches / len(relevant_keywords)) * 100
# Component 4: Regional relevance
# Videos trending in MENA, South Asia, Malaysia score higher
region_score = get_region_weight(trend_data.get('region', 'unknown')) * 100
# Component 5: Safety (quick LLM check)
safety_score = 100 if is_kid_safe(trend_data['title']) else 0
# Weighted sum
final_score = (
popularity_score * 0.35 +
velocity_score * 0.30 +
topic_fit_score * 0.20 +
region_score * 0.10 +
safety_score * 0.05
)
return {
'overall_score': final_score,
'pass_threshold': final_score >= 75,
'breakdown': {
'popularity': popularity_score,
'velocity': velocity_score,
'topic_fit': topic_fit_score,
'region': region_score,
'safety': safety_score
}
}
Example: Scoring a Trending Video
Trend: "How to teach kids Arabic letters at home"
Metrics:
- Views: 500,000
- Days: 7
- Keywords: Contains "teach", "kids", "Arabic"
- Region: US + Canada
- Safety: Clean
Scoring:
- Popularity: 50 (500k/1M capped)
- Velocity: 71 (500k views / 7 days = ~71k/day)
- Topic fit: 67 (3 of 5 keywords match)
- Region: 60 (US diaspora)
- Safety: 100 (clean)
Final: (50 × 0.35) + (71 × 0.30) + (67 × 0.20) + (60 × 0.10) + (100 × 0.05)
= 17.5 + 21.3 + 13.4 + 6 + 5
= 63.2 → FAIL (below 75 threshold)
Higher-Scoring Example
Trend: "تعليم القرآن للأطفال - طرق فعالة"
Metrics:
- Views: 2,000,000 (viral)
- Days: 3 (fast growth)
- Keywords: "قرآن", "أطفال", "تعليم" (all match)
- Region: MENA + South Asia
- Safety: Clean
Scoring:
- Popularity: 100 (capped)
- Velocity: 100 (2M/3 days = 666k/day, capped)
- Topic fit: 100 (3 of 3 keywords)
- Region: 90 (MENA + diaspora)
- Safety: 100
Final: (100 × 0.35) + (100 × 0.30) + (100 × 0.20) + (90 × 0.10) + (100 × 0.05)
= 35 + 30 + 20 + 9 + 5
= 99 → PASS! (excellent fit)
Human-in-the-Loop: Mandatory Approval
Before any video publishes, it goes to Slack for team review:
Slack Notification
🎥 [Pipeline] Ready for Review: Video #47
Title: "كيف تعلم ابنك حروف العربية بسهولة"
Topic Score: 89/100
Estimated Views (ML model): 85,000-120,000
[Preview Video] [View Transcript] [View Analysis]
Compliance Status: ✅ Pass
- No violence: ✓
- Age appropriate: ✓
- No IP violations: ✓
Actions: ✓ Publish | 🔄 Revise | ✗ Reject
Special Gates
- Quran content: Extra scholarly review
- New trends: Bonus manual review
- High velocity trends: Faster pipeline priority
Ports & Adapters Architecture
The pipeline is designed to swap providers without touching business logic:
# src/services/content_generation/interfaces.py
class TextGeneratorInterface:
def generate_script(self, topic: str, age_group: str) -> str:
pass
class OpenAIScriptGenerator(TextGeneratorInterface):
def generate_script(self, topic: str, age_group: str) -> str:
# Use OpenAI API
pass
class ClaudeScriptGenerator(TextGeneratorInterface):
def generate_script(self, topic: str, age_group: str) -> str:
# Use Anthropic API
pass
# At runtime, inject the right provider
script_generator = ClaudeScriptGenerator() # Easy to swap
script = script_generator.generate_script('تعليم العربية', '5-7')
Benefit: If OpenAI goes down, switch to Claude with one config change.
Results
Volume
- Input: 50-100 trending topics per week
- Threshold pass rate: ~20% (15-20 trends pass scoring)
- Published: ~3-4 videos per week
- Annual output: 150-200 videos
Performance (Actual Data)
- Average views per video: 12,000-45,000
- Average CTR: 8-12% (industry: 2-5%)
- Average watch time: 65-85% of video length (industry: 40-50%)
- Conversion (views → app installs): 3-5% (industry: 0.5-1%)
Cost
- AI generation per video: $3-5 (GPT, DALL-E, ElevenLabs)
- Human review: 15 min × $25/hour = $6.25
- YouTube hosting: Free
- Total per video: ~$10
- Cost per install: ~$2-3 (calculated from 3-5% conversion)
FAQ
Q: What if a generated script is inaccurate about Arabic? A: The human approval gate catches this. If the script has grammatical errors or cultural insensitivity, the reviewer selects "Revise" and provides notes. Pipeline regenerates with feedback.
Q: Does this violate YouTube's automation policies? A: No. We have human review before publishing (mandatory Slack gate). YouTube allows AI-assisted content as long as it's not fully automated without oversight.
Q: Can AI-generated videos rank well in search? A: Yes, if they're high-quality (which ours are). The algorithm doesn't penalize AI generation — it rewards watch time, CTR, and engagement. Our videos perform better than average.
Related reading
See how we generate learning content at scale and why multilingual reach matters for Arabic families.
