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Seyhun Akyurek
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Filter

AI-powered content moderation at scale — detect hate speech, nudity, violence, misinformation, and policy violations.

Azure AI Foundry
Azure Content Safety
Azure AI Vision
Python
FastAPI
Azure Database for PostgreSQL
Azure Kubernetes Service
99.2%
Detection Rate
100x faster
Review Speed
< 2%
False Positive
15+
Coverage

Project presentation

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Live system presentation

Filter · Agent Mesh

ORCHESTRATORMediaTextACTIVEVisualNODE 02AudioNODE 03CredibilityNODE 04EnforcementNODE 0512345678
Step 1Instant

Content Ingestion

Receive content from multiple channels and formats.

API submissionBatch uploadStream captureFormat normalization

Technical Design

Multi-modal content moderation at scale with policy-based enforcement. AI agents specialize in text, image, and video analysis with configurable rule engines.

System Components

Text Analyzer

NLP-based content classification

Visual Analyzer

Computer vision for imagery

Audio Analyzer

Transcription and audio analysis

Policy Engine

Configurable rule enforcement

Enforcement Agent

Action execution and escalation

Data Flow

Content Ingestion
Pre-Processing
Multi-Modal Analysis
Policy Evaluation
Decision Making
Action Execution
Appeal Handling

Technology Choice

Azure AI Foundry + LangChain

Azure AI Foundry provides multi-modal analysis capabilities. LangChain orchestrates complex moderation workflows with policy-based decision logic.

Alternatives Considered

Google Cloud Vision AI + Custom ML, AWS Rekognition + LlamaIndex

Core Frameworks

Azure AI Foundry

Multi-modal content analysis

LangChain

Moderation workflow orchestration

Azure Content Safety

Pre-built content moderation

Azure AI Vision

Image and video analysis

Implementation Plan

Multi-Modal Analysis

5 weeks
  • Text analysis
  • Image scanning
  • Video processing

Policy Framework

4 weeks
  • Rule engine
  • Severity tiers
  • Enforcement actions

Appeal System

3 weeks
  • Appeal intake
  • Human review
  • Resolution tracking

Analytics & Tuning

3 weeks
  • Performance metrics
  • False positive reduction
  • Model tuning

Key Milestones

Detection rate >99%
False positive <2%
Review speed 100x faster
15+ languages supported

Risk Mitigation

Conservative thresholds for sensitive categories
Human review for edge cases
Regular policy updates with feedback

Key Features

Text Moderation

NLP-powered detection of hate speech, harassment, spam, and policy violations in text.

Image Analysis

Computer vision for nudity, violence, graphic content, and policy-violating imagery.

Video Analysis

Frame-by-frame video moderation with audio transcript analysis.

Misinformation Detection

Fact-checking and credibility scoring for news and claims using knowledge graphs.

Appeal Management

Automated appeal handling with human-in-the-loop escalation for edge cases.

Policy Management

Configurable policy rules with severity tiers and automated enforcement actions.

How It Works

1

Content Ingestion

Instant

Receive content from multiple channels and formats.

API submission
Batch upload
Stream capture
Format normalization
2

Pre-Processing

< 5 seconds

Normalize and prepare content for analysis.

Format conversion
Transcription
OCR extraction
Metadata capture
3

Multi-Modal Analysis

< 10 seconds

Run content through specialized analysis agents in parallel.

Text analysis
Image scanning
Audio review
Video frames
4

Policy Evaluation

< 2 seconds

Map detected signals to applicable policies and severity levels.

Policy matching
Severity scoring
Context consideration
Edge case flagging
5

Decision Making

< 5 seconds

Make moderation decision based on policy rules and confidence scores.

Auto-decide
Confidence threshold
Human escalation
Context review
6

Action Execution

Instant

Execute moderation actions (approve, warn, remove, escalate).

Content action
User notification
Logging
Appeal info
7

Appeal Handling

1-24 hours

Process user appeals with additional review and context.

Appeal intake
Re-review
Human escalation
Resolution
8

Learning & Tuning

Ongoing

Continuous model improvement from decisions and feedback.

Feedback loop
Model tuning
Policy updates
Performance tracking

Multi-Agent Architecture

Text Agent

Analyzes text content for policy violations using NLU models.

  • Hate speech detection
  • Harassment identification
  • Spam filtering
  • Toxicity scoring
  • Context analysis

Visual Agent

Analyzes images and video frames for policy-violating visual content.

  • Nudity detection
  • Violence detection
  • Graphic content
  • Logo detection
  • OCR screening

Audio Agent

Analyzes audio content for policy violations and transcription.

  • Transcription
  • Sentiment analysis
  • Music detection
  • Explicit content
  • Language detection

Credibility Agent

Evaluates content credibility and detects potential misinformation.

  • Fact checking
  • Source verification
  • Claim detection
  • Knowledge graph
  • Confidence scoring

Enforcement Agent

Executes moderation actions based on violation severity and policies.

  • Content removal
  • Warning display
  • Account actions
  • Appeal routing
  • Escalation management

Use Cases

Social Media Platforms

Moderate user-generated content at scale across text, images, and video.

Marketplace Listings

Screen product listings for prohibited items and misleading content.

Online Gaming

Moderate chat, voice, and user profiles in gaming environments.

News & Publishing

Verify content accuracy and flag potential misinformation.

Education Platforms

Ensure safe learning environments with age-appropriate content filtering.

Enterprise Communications

Monitor internal communications for compliance and policy adherence.

Interested in Filter?

Get in touch to discuss how this solution can be tailored to your needs.