Tensorflow AI Content Moderation
LANCER
Lancer :
Ideal for quick, focused engagements
Team: ≤ 2 resources
Duration: ≤ 3 weeks
Indicative Budget: ≤ $3,000 USD*
Use Cases: MVPs, prototypes, simple websites, bug-fix sprints
Indicative Pricing: ~$15–$25/hr per resource*
Growth :
Best suited for growing products or structured builds
Team: 2–5 resources
Duration: Up to 4 months
Indicative Budget: Up to $30,000 USD*
Use Cases: Feature-rich web apps, integrations, custom dashboards
Indicative Pricing: ~$15–$35/hr per resource*
Enterprise :
Tailored for high-scale, complex or long-term initiatives
Team: 5+ resources
Duration: More than 4 months
Indicative Budget: Starts from $30,000+ USD*
Use Cases: End-to-end platforms, SaaS products, compliance-heavy systems
Indicative Pricing: ~$25–$50/hr depending on roles*
(Development)
Project Description
A fast-scaling digital platform tackling political misinformation and harmful user posts needed an automated way to classify and moderate content in real time. They sought a solution that could handle both text and image-based posts with high accuracy and quick response. The platform required a reliable moderation system that could adapt to dynamic and sensitive political discourse online.
Scope Of Work
The client sought a solution from Oodles to develop a content moderation system that could classify user-submitted posts, text, image, or both—and determine if the content required moderation. They needed a tool with the help of Oodles that could process inputs, assess toxicity levels, and flag politically sensitive content using AI and machine learning techniques across various content types.
Our Solution
To address the client’s needs, a machine learning-powered moderation system was developed and deployed with real-time content scanning capabilities. The strategy focused on combining Natural Language Processing and image analysis for comprehensive moderation coverage.
Key Features Implemented:
- Multimodal Input Handling: Users can enter comments as text, images, or both for moderation analysis.
- Toxicity Score Generation: The system returns a toxicity score and classifies content as safe or requiring moderation.
- OCR Integration with EasyOCR: Enables extraction and analysis of text from image-based posts.
- Lightweight Django Web Interface: Built a responsive interface for content analysis with minimal user friction.
- Model Integration: Utilized TensorFlow, PyTorch, and Transformer models for robust, scalable predictions.
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