Backend Pipeline Diagrams

Visual architecture — diagrams only

Act 1 — Full System Overview
INPUT
→
Pipeline A · News Intelligence
Ingest
→
Analyze
→
Validate
→
Pipeline B · Video Synthesis
Synthesize
→
QA
→
Deploy
→
USER
↺   REFINEMENT DATA — Model Feedback Loop
① Ingest
② Analyze
③ Validate
④ Synthesize
⑤ QA
⑥ Deploy
⑦ Learn
Act 2 — Multi-Source Data Ingestion
📰
News APIs
📡
RSS Feeds
📰
Licensed Pubs
🐦
Social Signals
↓
🗄️ Unified Data Pool
Hundreds of articles / min · Structured & timestamped
Act 3 — Deduplication & Clustering
Raw Pool
→
Aggregation Engine
🤖 sub_agent_1
Deduplication
→
Aggregation Engine
🤖 sub_agent_2
Clustering
→
Story Clusters
Arrow = execution order, not state sharing
Act 4 — Structured Content Parsing
Story Cluster
→
Extraction Module
🏷️  HEADLINE
📝  BODY TEXT
📊  METADATA  Author · Date · Source
🔑  KEY ATTRS  Keywords · Entities
→
OUTPUT
{
  "headline": "...",
  "body": "...",
  "metadata": {…},
  "keywords": […],
  "entities": […]
}
Act 5 — AI Analysis Engine
NLP Agent
🤖
Context Mapping
Produce valid,
structured analysis
→
SUMMARIZER
Quick · Standard · Deep
SENTIMENT
Positive / Negative / Neutral
FACT CHECK + BIAS
Cross-reference · Score · L/C/R%
Parallel
→
{
  "summary": "...",
  "sentiment": "negative",
  "confidence": 87,
  "bias": {"L":40,"C":35,"R":25},
  "agreement": "HIGH"
}
Confidence Score: 87 / 100
Bias: L 40% · C 35% · R 25%
✅ Trusted: Score ≥ 70
⚠️ In Review: Score < 70
Act 6 — Validation Layer + API Output
Content Object
→
sub_agent_1
🤖
Schema Validation
→
sub_agent_2
🤖
Consistency Check
→
sub_agent_3
🤖
Reliability Score Gate
Arrow = execution order, not state sharing
→
❌ FAIL → Re-queue / Discard
✅ PASS → API Response Ready
{
  "status": "verified",
  "summary": "...",
  "sentiment": "negative",
  "accuracy": "verified",
  "confidence": 87,
  "timestamp": "2025-06-12T08:34Z"
}
✅ Schema Valid
✅ Consistent
✅ Score ≥ 70
Act 7 — Multi-Variant Generation
✅ JSON Input
→
Multi-Variant Generation Module
V1
15s
V2
15s
V3
15s
V4
15s
V5
15s
…
MP4
WebM
MPA
5–10 variants per story · Multi-style rendering · Format diversification
Act 8 — QA Automation
QA Orchestrator
🤖
Controls both tools
→
VLM
Vision-Language Model
Visual defect detection
+ content verification
ASR
Automatic Speech Recognition
Audio validation
+ transcription check
Simultaneously
→
❌ FAIL → Re-queue to Gen Module
✅ PASS → Approved Videos
Act 9 — Deployment + Optimization Layer
✅ Approved Videos
→
sub_agent_1
🤖
A/B Testing Distribution
→
sub_agent_2
🤖
Behavioral Metrics Aggr.
→
sub_agent_3
🤖
Model Feedback Loop
Arrow = execution order, not state sharing
📊
Interaction
Metrics
⏱️
Dwell
Time
✅
Completion
Rate
💬
Qualitative
Feedback
↺   REFINEMENT DATA → back to Multi-Variant Generation Module
Act 10 — Progressive Revelation Delivery System
Personalization Engine
🤖
Behavior + Location + Interests
→
Layer 0
🎬 AI Video Feed
8–15s · auto-personalized
↓ tap
Layer 1
🪝 Hook Card
Location + behavior hook
↓ tap
Layer 2
📰 Speed Article
Quick · Standard · Deep
↓ tap
Layer 3
🔍 Trust Panel
Score · Bias · Sources
Act 11 — Full System Schematic
INPUT
→
INGEST
→
DEDUP
→
PARSE
→
ANALYZE
→
VALIDATE
→
SYNTHESIZE
→
QA
→
A/B TEST
→
METRICS
→
FEEDBACK
→
USER
↺   REFINEMENT DATA loops back to SYNTHESIZE