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Development of AI-Powered HyperAutomated Data Scraping Platform for Enhanced Media Monitoring

vstorm.co

Advertising & marketing

Media

Challenges in Traditional Data Scraping for PR Analytics

Existing data scraping methods require excessive budget, time, and development resources while delivering insufficient accuracy. Manual monitoring of thousands of news sources is labor-intensive, slow, and unable to scale effectively to meet growing client demands.

About the Client

Boutique PR agency specializing in data-driven public relations and digital PR solutions

Objectives for AI-Driven Data Scraping Solution

Reduce time and cost of data collection by 70%
Achieve 95%+ accuracy in unstructured data extraction
Enable scalable monitoring of 1M+ articles annually
Automate context-aware sentiment and media presence analysis
Ensure compliance with copyright and IP regulations

Core System Functionalities

Playwright-based web scraping engine
NLP-driven context recognition and entity extraction
LLM-enhanced sentiment analysis module
Cloud-native horizontal scaling architecture
Automated weekly update pipeline with Redis logging
Pydantic-validated data processing workflows

Technology Stack Requirements

Python (NLP/ML stack)

Playwright

TensorFlow/PyTorch

AWS/GCP cloud services

Redis

Celery Beat

System Integration Needs

News platform APIs
Cloud storage services
Redis message broker
Monitoring/alerting systems

Operational Requirements

99.9% system uptime SLA
Linear scalability to 10M+ articles/month
GDPR-compliant data handling
Real-time processing latency <2s

Expected Business Impact of HyperAutomated Data Scraping

Enables real-time media monitoring with 80% reduction in manual labor costs, automated weekly insights delivery, and enhanced client reporting through nuanced sentiment analysis. Scalable infrastructure supports market expansion while maintaining 99.9% data accuracy compliance with legal frameworks.