A Case Study On
How Can We Help?
/ Our Case Study / Revolutionizing Anti-Virus Solutions with AI Collaboration Wardwiz
Introduction
Technical Stack
Technology Used
PHP, MySQL, Stripe, Android-Native, iOS
Team Size
18
Methodology
Agile
Project Duration
2 Years
Background
Partnerships
Highlights
Internet Soft collaborated with WardWiz to develop an advanced anti-virus solution, Â showcasing expertise in cybersecurity, AI, and machine learning.
Integrated intelligent threat detection and anomaly detection capabilities for real-time protection against emerging threats.
Leveraged AI and machine learning to analyze user behavior, detect anomalies, and prevent breaches.
Developed a cloud-based security intelligence system using global threat feeds for up-to-date protection against evolving malware.
User-friendly interface and comprehensive reporting features for proactive threat monitoring.
Product Development Process:
Requirement Gathering and Analysis
Internet Soft and WardWiz collaborated to understand specific customer requirements and industry trends. They conducted extensive research to identify advanced AI and machine learning techniques that could enhance the anti-virus product's threat detection, malware analysis, and anomaly detection capabilities.
AI-Driven Threat Detection
Behavioral Analysis and Anomaly Detection
Protect your digital world today – Explore our Antivirus software solutions now!
Cloud-Based Security Intelligence
User-Friendly Interface and Reporting
Continuous Learning and Improvement
Marketing and Success
Conclusion
69%
Efficiency Increased By
10000+
Global Customers
5X+
Faster Release Cycle
Explore More Blogs

Retrieval-Augmented Generation (RAG)
What is Retrieval-Augmented Generation (RAG)? Retrieval-Augmented Generation (RAG) is an approach that enhances AI systems

Model Monitoring & Drift Detection: Ensuring Reliable AI in Production
Artificial Intelligence models rarely fail abruptly—they degrade gradually. As data patterns evolve, user behavior shifts, and

MLOps Lifecycle & Best Practices for Scalable AI Systems
Machine learning initiatives often fail not because of weak models, but because of poor operationalization. Enterprises