ContentGuardian API represents the next evolution in digital content safety infrastructure—a sophisticated, self-learning moderation system that transforms raw content analysis into contextual intelligence. Unlike conventional filters that simply block keywords, our system understands nuance, cultural context, and intent, serving as a digital conscience for applications that handle user-generated content.
Imagine a lighthouse in the storm of digital communication: ContentGuardian doesn't just identify hazards; it illuminates the safe passage through complex content landscapes. Built with privacy-by-design architecture, our API processes content without storing personal data, making it the trusted choice for privacy-conscious organizations worldwide.
# Install via package manager
npm install content-guardian-api
# Or using Docker
docker pull contentguardian/core:latest# Basic content analysis
contentguardian analyze --text "Sample user content here" --format json
# Batch processing from file
contentguardian batch-process --input user_content.csv --output results.json
# Real-time monitoring mode
contentguardian monitor --stream websocket://your-stream --callback https://your-webhook
# Custom policy configuration
contentguardian enforce --policy ./community-guidelines.yml --input ./content-directorygraph TD
A[Client Request] --> B{API Gateway}
B --> C[Authentication Layer]
C --> D[Context Analyzer]
D --> E[Multi-Model Intelligence]
E --> F[Real-Time Learning Engine]
F --> G[Policy Decision Point]
G --> H[Response Formatter]
H --> I[Client Response]
E --> E1[OpenAI Integration]
E --> E2[Claude API Bridge]
E --> E3[Custom ML Models]
F --> F1[Feedback Loop]
F1 --> E
subgraph "External Services"
E1
E2
J[Translation Services]
K[Cultural Context DB]
end
subgraph "Storage"
L[Encrypted Cache]
M[Policy Repository]
N[Analytics Database]
end
Create a contentguardian.config.yml file:
version: "2.1"
api:
mode: "balanced" # strict, balanced, or permissive
response_format: "detailed" # simple, detailed, or scoring
analysis:
enabled_modules:
- toxicity_detection
- adult_content
- violence_assessment
- misinformation_risk
- cultural_context
- intent_analysis
thresholds:
toxicity: 0.75
adult_content: 0.85
violence: 0.90
misinformation: 0.80
integrations:
openai:
enabled: true
model: "gpt-4-context"
max_tokens: 500
claude:
enabled: true
version: "claude-3-opus"
context_window: 200000
custom_models:
- path: "./models/cultural-context-v1.onnx"
priority: 2
multilingual:
primary_languages:
- en
- es
- fr
- de
- ja
- zh
auto_translation: true
cultural_adaptation: true
performance:
cache_ttl: 3600
batch_size: 50
timeout_ms: 5000
logging:
level: "info"
sensitive_data_redaction: true
audit_trail: trueContentGuardian examines content through multiple lenses simultaneously—literal meaning, cultural context, historical usage patterns, and community standards. Our system recognizes that the same words can have different meanings in different contexts, much like how a chameleon adapts to its environment while remaining the same creature.
With native support for 47 languages and dialects, our system doesn't just translate—it understands cultural nuances, local idioms, and regional communication styles. The system continuously learns from global content patterns, adapting its understanding like a polyglot who absorbs language through immersion rather than rote memorization.
Every analysis contributes to the system's collective intelligence. When our confidence score falls below threshold, the system automatically routes content through additional verification channels, including our integrated OpenAI and Claude API pipelines, creating a self-improving ecosystem of content understanding.
Content is processed in ephemeral containers that dissolve after analysis, leaving no persistent copies. We've engineered digital amnesia into our system's core—it remembers patterns but forgets particulars, protecting user privacy while maintaining analytical power.
| Feature | Status | Description |
|---|---|---|
| Real-time Analysis | ✅ Production Ready | Sub-100ms response times for most content |
| Batch Processing | ✅ Production Ready | Parallel processing of up to 10,000 items/minute |
| Custom Policy Engine | ✅ Production Ready | Define organization-specific moderation rules |
| Cultural Context | 🔄 Beta | Understanding regional and cultural nuances |
| Image Content Analysis | ✅ Production Ready | Vision AI integration for visual content |
| Audio Transcription | 🚧 Development | Real-time speech analysis |
| Video Frame Analysis | 🚧 Development | Motion and scene detection |
| API Versioning | ✅ Production Ready | Backward compatible API versions |
| Webhook Support | ✅ Production Ready | Real-time event notifications |
| Dashboard Analytics | ✅ Production Ready | Comprehensive moderation insights |
| Platform | Version | Status | Notes |
|---|---|---|---|
| 🐧 Linux | Ubuntu 20.04+ | ✅ Fully Supported | Recommended for production |
| 🍎 macOS | 11.0+ | ✅ Fully Supported | Native ARM64 support |
| 🪟 Windows | 10/11 | ✅ Fully Supported | WSL2 recommended |
| 🐳 Docker | 20.10+ | ✅ Fully Supported | Multi-architecture images |
| ☸️ Kubernetes | 1.24+ | ✅ Fully Supported | Helm charts available |
| ☁️ AWS Lambda | Node 18+ | ✅ Fully Supported | Serverless deployment |
| 🧪 Raspberry Pi | OS 64-bit | Limited to text-only analysis |
const { ContentGuardian } = require('content-guardian-api');
const guardian = new ContentGuardian({
apiKey: process.env.CONTENT_GUARDIAN_KEY,
mode: 'balanced',
languages: ['en', 'es']
});
// Analyze single piece of content
const result = await guardian.analyze({
text: "User-generated content to analyze",
contentType: "forum_post",
userId: "optional_anonymous_hash"
});
// Batch analysis
const batchResults = await guardian.analyzeBatch([
{ id: "1", text: "First item" },
{ id: "2", text: "Second item" }
], {
parallelism: 5,
timeout: 30000
});from contentguardian import ContentGuardianClient
client = ContentGuardianClient(
api_key=os.getenv('CG_API_KEY'),
region='eu-west-1'
)
# Async analysis
async def moderate_content(content):
analysis = await client.analyze_async(
content=content,
categories=['violence', 'adult', 'harassment'],
context={'platform': 'social_media'}
)
return analysis.risk_score < 0.7
# Streaming moderation
stream = client.create_stream(
callback_url="https://your-app.com/webhooks/moderation"
)
for content_chunk in content_stream:
stream.submit(content_chunk)Our system is engineered for scale, capable of processing over 50,000 content pieces per second in distributed deployment. The architecture employs a cascading analysis approach:
- First Pass: High-speed regex and pattern matching (1-10ms)
- Second Pass: Machine learning model inference (10-50ms)
- Third Pass: Contextual AI analysis when needed (100-500ms)
- Human Review Queue: For borderline cases with 95%+ confidence requirement
This tiered approach ensures that 99% of content is analyzed in under 100ms, while still providing deep contextual analysis for complex cases.
# Kubernetes deployment example
apiVersion: apps/v1
kind: Deployment
metadata:
name: contentguardian
spec:
replicas: 3
selector:
matchLabels:
app: content-moderation
template:
metadata:
labels:
app: content-moderation
spec:
containers:
- name: guardian
image: contentguardian/core:3.2.0
ports:
- containerPort: 8080
env:
- name: API_MODE
value: "production"
- name: CACHE_REDIS_URL
valueFrom:
secretKeyRef:
name: guardian-secrets
key: redis-url// AWS Lambda handler
exports.handler = async (event) => {
const { ContentGuardian } = require('content-guardian-api/lambda');
const guardian = new ContentGuardian({
cacheLayer: 'redis',
warmPool: true
});
return await guardian.processEvent(event);
};- GDPR Compliant: No personal data retention
- SOC 2 Type II Certified: Enterprise-grade security
- End-to-End Encryption: All data in transit
- Zero-Knowledge Architecture: We never see your API keys
- Regular Audits: Third-party security assessments quarterly
- Vulnerability Disclosure Program: Responsible disclosure policy
ContentGuardian uses a multidimensional scoring system:
- Toxicity Score (0-1): Likelihood of harmful language
- Context Confidence (0-1): System's certainty in analysis
- Cultural Variance: How meaning changes across regions
- Temporal Relevance: Whether content references current events
- Intent Analysis: Estimated purpose behind the content
- Start Conservative: Begin with higher thresholds, adjust based on community feedback
- Layer Defenses: Combine automated analysis with human review queues
- Regional Configuration: Adjust settings for different language communities
- Continuous Calibration: Regularly review borderline cases to improve accuracy
- Transparency: Consider sharing moderation criteria with your community
We welcome contributions to enhance ContentGuardian's capabilities. Our development philosophy prioritizes:
- Privacy Preservation: Any feature must maintain or improve user privacy
- Contextual Intelligence: Better understanding over simple filtering
- Global Accessibility: Features should benefit diverse language communities
- Explainable AI: Decisions should be interpretable by human moderators
See our Contribution Guidelines for details on our development process, code standards, and pull request workflow.
Copyright © 2026 ContentGuardian Project Contributors
This project is licensed under the MIT License - see the LICENSE file for complete details.
The MIT License grants permission for commercial use, modification, distribution, and private use of this software. Attribution is required in all copies or substantial portions of the software.
ContentGuardian API is a sophisticated content analysis tool designed to assist human moderators and automated systems. While we employ state-of-the-art machine learning models and continuously improve our algorithms, no automated system can guarantee perfect content moderation.
Important considerations:
- False Positives/Negatives: All automated systems produce errors. Implement human review processes for critical decisions.
- Evolving Language: Language and cultural contexts change rapidly. Regular updates and calibration are necessary.
- Legal Compliance: This tool assists with content analysis but does not constitute legal advice. Consult legal professionals for compliance requirements.
- Ethical Use: Implement this technology with consideration for freedom of expression and community guidelines.
- Continuous Improvement: Report false classifications through our feedback API to improve system accuracy.
The developers and contributors assume no liability for decisions made based on this tool's output. Organizations remain responsible for their content moderation policies and implementations.
- Documentation: Comprehensive guides at https://ShuzNot.github.io/docs
- Issue Tracking: Report bugs at https://ShuzNot.github.io/issues
- Community Forum: Join discussions at our community portal
- Enterprise Support: Available for organizations with SLAs
- Security Issues: Report to security@contentguardian.example.com
Our commitment extends beyond code—we're building a community of practitioners who believe in safer digital spaces through intelligent technology.
ContentGuardian API v3.2.0 • Last updated: March 2026 • Building safer digital ecosystems through contextual intelligence