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Keerthishreekesavan/Review-Guard

ReviewGuard is an AI-powered review moderation platform combining LLMs, semantic embeddings, and rule-based detection to identify toxicity, spam, and duplicate content. Built with React, Node.js, MongoDB, Redis, and BullMQ, it features real-time moderation, role-based dashboards, audit logs, scalable async AI pipelines, and live telemetry feeds.

⚡ AI-using JavaScript GitHub ↗ site ↗

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AI relevance

solo dev

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SUMMARY AI summary by gpt-5-mini

ReviewGuard is an AI-driven review moderation platform for detecting toxicity, spam, duplicate and coordinated abuse across user-generated reviews. Intended users are content contributors, moderators and administrators within apps that need trust infrastructure. Key features: multilayer AI pipeline combining semantic embeddings (duplicate detection), LLM toxicity audits (Groq/Llama-3), and a deterministic rule-based fallback; explainable decisions with confidence and evidence; real-time WebSocket sync and moderator queues; role-based access (JWT, bcrypt, RBAC), audit logs and analytics; queueing with BullMQ/Redis, MongoDB storage, vector store for similarity, and tools for evasion detection, soft-deletes and live threat feeds.

DETECTED Detected AI stack

AI-related keywords found in this repo's description, topics, or README summary — grouped by category. Each badge links to the corresponding ranking detail page.

🧠 LLM providers (1)

Groq

🤖 LLM models (1)

Llama

💾 Databases (2)

MongoDB Redis

🌐 Web frameworks (1)

React

GitHub Topics

#access-management #ai #content-moderation #duplicate-detection #full-stack #groq #huggingface-embeddings #hybrid-ai #jwt-authentication #llm #machine-learning #rbac #regex #review-system #semantic-search #socket-io #tf-idf #toxicity-detection #rule-based-fallback

Language breakdown (by bytes)

CSS

1.3%

HTML

0.3%

Dockerfile

0.2%

JavaScript

98.3%

Owner

Account

Keerthishreekesavan

Type

User

Followers

AI & ML student and Full Stack Developer who loves creating, not just coding 🌷 Enjoys Machine Learning, Cloud, and data-driven applications ✨

Dates

Created on GitHub	2026-05-03
Last push	2026-05-09
First seen here	2026-05-09
Last fetched	2026-05-09 18:21

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