INTERNAL DOCUMENTATION > ENGINEERING > CONTENT PIPELINE

Content Processing Workflow

End-to-end architecture detailing how submissions are ingested, validated, AI-augmented, peer-reviewed, and published to the global knowledge graph. Designed for transparency, auditability, and academic rigor.

v3.2.1 Last Updated: Nov 2025 Avg. Cycle: 4.2 Days Zero-Trust Architecture
7
Processing Stages
3
Verification Layers
98.7%
Auto-Rejection Rate (Noise)
< 24h
Time to First Review

Pipeline Architecture

High-level data flow from contributor submission to live publication.

[SUBMISSION]
→
[INGEST & SANITIZE]
→
[AI PRE-SCREEN]
→
[EXPERT QUEUE]
→
✓ APPROVE ↻ REVISE ✗ REJECT
→
[KNOWLEDGE GRAPH SYNC]
→
[PUBLISH]

Step-by-Step Processing

Detailed breakdown of each workflow stage, including validation rules and automation triggers.

1

Ingestion & Sanitization

AUTOMATED

Raw markdown/HTML submissions are parsed, stripped of executable code, and normalized into our internal schema. Attachments are virus-scanned and metadata is extracted.

Schema
AE-Content v2
Sanitization
DOMPurify + Custom Rules
Latency
~800ms
2

AI Pre-Screening & Classification

ML PIPELINE

NLP models analyze structure, factual density, citation format, and tone. Content is tagged by discipline, difficulty level, and confidence score. Low-quality or policy-violating drafts are auto-flagged.

Models
Aevum-Classifier-3 + FactCheck-Net
Threshold
≥0.82 for fast-track
Output
JSON-LD + Taxonomy Tags
3

Expert Assignment & Review

HUMAN-IN-LOOP

Verified domain experts are matched via skill graphs. Reviewers assess accuracy, neutrality, completeness, and adherence to style guides. Dual-blind review is enforced for high-impact topics.

Reviewer Pool
180K+ Verified
SLA
48-72 hours
Dispute Res
Arbitration Queue
4

Cross-Reference & Fact Verification

COMPUTED

Every claim is matched against primary sources, academic databases, and our archival corpus. Contradictions trigger automated alerts. Confidence intervals are calculated per assertion.

Sources
12M+ Peer-Indexed
Coverage
≥94% per article
Audit
Immutable Hash Log
5

Knowledge Graph Integration

GRAPH ENGINE

Entities, relations, and temporal data are extracted and merged into the global RDF graph. Disambiguation resolves naming collisions. Backlinks and forward-references are auto-generated.

Format
OWL 2 / SHACL Validated
Sync
Real-time Incremental
Traversal
Sub-50ms Queries
6

Publication & CDN Distribution

DEPLOYMENT

Rendered HTML is cached, translated via neural MT (human-post-edited for tier-1 languages), and pushed to edge nodes. SEO metadata, accessibility tags, and version tags are attached.

Languages
140+ Active
Edge Pop
42 Regions
TTFB
< 120ms Global
7

Continuous Monitoring & Retraining

FEEDBACK LOOP

Post-publication, articles are monitored for citation drift, new counter-evidence, and community feedback. Models are retrained quarterly. Deprecation notices are auto-applied to outdated claims.

Drift Detect
Weekly Batch
Retraining
Quarterly
Compliance
ISO/IEC 42001

Quality Assurance & Compliance

Rigorous controls ensuring academic integrity, neutrality, and regulatory alignment.

🛡️ Bias Mitigation

Automated tone analysis flags subjective language. Multi-regional reviewer panels ensure cultural neutrality. Disaggregated approval metrics prevent systemic bias.

📜 Provenance Tracking

Every edit, citation, and reviewer action is logged to an immutable ledger. Contributors retain attribution. Version diffs are publicly auditable.

🔐 Data Sovereignty

Regional data routing complies with GDPR, CCPA, and emerging AI acts. Sensitive classifications are geo-fenced. Encryption at rest & in transit (AES-256/TLS 1.3).

♿ Accessibility & Standards

WCAG 2.2 AA compliant markup. Screen-reader optimized. Semantic HTML5 structure. Keyboard navigation and reduced-motion support enabled by default.

Need Technical Documentation?

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