Datathon Project : Parental Monitoring - Superr

NLP-Based Search Query Monitoring

Role

NLP Engineer & Backend Developer

Industry

Technology

Duration

3 months

Stage 1: Understanding the Problem & Market Research

Kids increasingly use search engines to explore the internet, often encountering inappropriate content.
Existing parental control tools were either too restrictive or lacked intelligent categorization.
Goal: Create a smart monitoring system that flags questionable searches while allowing safe exploration.

Key Research Findings:

62% of parents worry about their children encountering inappropriate content online.
Most filtering tools rely on pre-set lists, missing context in evolving internet searches.

Stage 2: Developing the AI-Powered Categorization System

Used NLP models to classify search queries into categories like Safe, Questionable, and NSFW.
Implemented a real-time search monitoring engine that provides instant alerts for flagged content.
Designed a parent dashboard for monitoring search activity.

✅ Key Features:
✔️ Real-time NLP-Based Categorization – Queries analyzed & classified instantly.
✔️ Parental Alerts & Dashboard – Sends notifications when flagged searches occur.
✔️ Customizable Filtering Settings – Allows parents to fine-tune monitoring preferences.

Stage 3: Building & Testing the AI Model

Implemented a BERT-based text classification model for highly accurate query categorization.
Used a dataset of 100,000+ search queries to train the model for safe, questionable, and explicit content.
Integrated the model with a Django backend API to handle real-time search processing.

🛠️ Tech Stack:

Backend: Django, FastAPI
NLP Model: BERT (Transformers)
Database: PostgreSQL
Frontend: React.js (Parent Dashboard)

Stage 4: User Testing & Iterations

Conducted mock tests with 200+ sample search queries to evaluate model accuracy.
Improved false positive rates by 30%, ensuring better classification.
Designed an explainability layer so parents could understand why a search was flagged.

📊 Impact:
✅ Classified search queries with 92% accuracy.
✅ Reduced exposure to harmful content by 70% in test environments.
✅ Provided parents with real-time insights & control over their child’s internet activity.

Stage 1: Understanding the Problem & Market Research

Kids increasingly use search engines to explore the internet, often encountering inappropriate content.
Existing parental control tools were either too restrictive or lacked intelligent categorization.
Goal: Create a smart monitoring system that flags questionable searches while allowing safe exploration.

Key Research Findings:

62% of parents worry about their children encountering inappropriate content online.
Most filtering tools rely on pre-set lists, missing context in evolving internet searches.

Stage 2: Developing the AI-Powered Categorization System

Used NLP models to classify search queries into categories like Safe, Questionable, and NSFW.
Implemented a real-time search monitoring engine that provides instant alerts for flagged content.
Designed a parent dashboard for monitoring search activity.

Stage 3: Building & Testing the AI Model

Implemented a BERT-based text classification model for highly accurate query categorization.
Used a dataset of 100,000+ search queries to train the model for safe, questionable, and explicit content.
Integrated the model with a Django backend API to handle real-time search processing.

🛠️ Tech Stack:

Backend: Django, FastAPI
NLP Model: BERT (Transformers)
Database: PostgreSQL
Frontend: React.js (Parent Dashboard)

Stage 4: User Testing & Iterations

Conducted mock tests with 200+ sample search queries to evaluate model accuracy.
Improved false positive rates by 30%, ensuring better classification.
Designed an explainability layer so parents could understand why a search was flagged.

Stage 5: Hackathon Presentation & Implementation in Superr.app

Presented the project at the hackathon demo day, receiving positive feedback for its practical impact.
Adopted by Superr.app as part of their AI-driven parental monitoring system, helping parents filter and monitor children’s online searches.
Proposed a browser extension & mobile app version for better accessibility.
Open-sourced part of the search classification API for future development.

📎 Outcome:
✅ Superr.app implemented Parental AI, making it a real-world product in online child safety.
✅ Won recognition at the hackathon and received interest from parental control software firms.

Reflections

This project challenged me to blend AI, NLP, and real-world problem-solving. It reinforced the importance of ethical AI development and building models that add real value while respecting privacy concerns.