Welcome to Netzary Infodynamics !

Netzary Logo

AI Driven Approach

AI/ML helps us better services
Machine Learning and AI

Our Secops and MDR services extensively use chatbots deriving intelligence from SIEM tools

Netzary has been leveraging AI/ML for improving our managed services outcome since 2023. It has been baby steps so far.Idea of sharing this document is so that customers understand the edge we are having over competition

1. Predictive Maintenance and Proactive Monitoring

Predictive Maintenance : We have started using AI to data from servers and systems to predict failures before they happen. This allows MSPs to perform maintenance activities proactively, reducing downtime and improving client satisfaction.

Proactive Monitoring : AI-powered monitoring tools can identify anomalies and potential issues in real-time, enabling quick resolution before they escalate into major problems.

2. Automated Support and Helpdesk

Chatbots and Virtual Assistants : We have started integrating chat bot based virtual assistants for select customers to reduce cost of operations.

Incident Management: Machine learning is being be used to categorize and prioritize support tickets based on historical data, ensuring critical issues are addressed promptly.

3. Enhanced Security

Threat Detection and Response : We have started deploying AI for analyzing network traffic and user behavior to detect unusual patterns that may indicate a security threat. Machine learning models are continuously being used to  improve threat detection.

Automated Security Updates : We are also using AI to automate the patch management process, ensuring that systems are always up-to-date with the latest security patches without manual intervention.

4. Optimization of IT Resources

Resource Allocation :We have started as of June 2024, employing Machine learning algorithms to optimize the allocation of IT resources based on usage patterns, ensuring that resources are used efficiently and reducing costs.

Capacity Planning : We are also using AI to predict skillsets demand for future.

5. Improved Decision Making

Data Analytics: We have already started using AI to process large volumes of data to provide actionable insights.These insights for strategic decision-making, such as identifying new service opportunities or areas for improvement should see results in next few months..

Customer Insights : Machine learning models can analyze customer data to understand behavior and preferences, allowing MSPs to tailor their services to meet customer needs better.

6. Service Automation

Automated Workflows : We have started using basic learning models in our automation workflows.

Intelligent Automation : Combining AI with robotic process automation (RPA) can further enhance automation capabilities, enabling more complex and intelligent task automation.

We are also offering our experience to help other MSPs and service providers to deliver better. These are consulting steps we offer our friends in the industry

  1. Identify Key Areas : Determine which aspects of your services can benefit most from AI/ML.

  2. Data Collection : Ensure you have the necessary data to train AI/ML models. This may involve setting up data collection systems if they aren't already in place.

  3. Choose the Right Tools : Select AI/ML tools and platforms that fit your needs. Popular options include TensorFlow, PyTorch, and various AI services from cloud providers like AWS, Azure, and Google Cloud.

  4. Pilot Projects : Start with small pilot projects to test the effectiveness of AI/ML solutions in your environment.

  5. Scale Up : Once proven, gradually scale up AI/ML implementations across your service offerings.

  6. Continuous Improvement : Help you continuously monitor and refine AI

and ML models to adapt to changing conditions and improve their performance over time.

By strategically implementing AI and ML technologies, we are significantly enhancing our service offerings, improving operational efficiency, and delivering higher value to their customers. The key to success lies in starting with well-defined projects, continuously refining AI/ML models, and integrating these technologies seamlessly into existing workflows.