Zielbox AIOps Offering

At our company, we prioritize practicality over theory, emphasizing the direct implementation of tools and technologies to transform your infrastructure. Our aim is to help you seamlessly transition your IT infrastructure onto an AIOps (Artificial Intelligence for IT Operations) stack.

We possess a deep understanding of the ITIL (Information Technology Infrastructure Library) framework and recognize the importance of incident and root cause management in today's distributed microservice environment. As a result, we skillfully integrate logging, metric, and tracing solutions into our AIOps implementation. This ensures that your AIOps setup is equipped with comprehensive capabilities to effectively manage incidents and identify root causes in this modern ecosystem.

By focusing on practical implementation and leveraging our expertise in AIOps and the ITIL framework, we enable you to achieve a well-rounded AIOps solution that optimizes your infrastructure and enhances operational efficiency.

What are the Zielbox AIOps offering:

We usually get engaged with clients in between 3 Month to 6 Month that includes understanding their existing workflow so that we can reconfigure things as per AIOps requirement.

We usually bring:

1. Right Scripts and Code,

2. AIOps Workflow

3. Domain specific AI/ML Algorithms for IT Infra

4. Integration into existing System through Infra as Code

5. Observability Boards

6. Configures CI/Cd workflows for AIOps

Insights into the AIOps work that Zielbox will bring in for you:

Models used in IT infrastructure management can be categorized into various types based on the specific task they are designed to perform. Here are some common categories of models used in IT infrastructure management:

1. Predictive models: These models use historical data to predict future events or trends in IT infrastructure, such as server downtime or network traffic spikes.

2. Anomaly detection models: These models use statistical methods to identify abnormal behavior in IT infrastructure, such as unusual traffic patterns or unexpected changes in system performance.

3. Classification models: These models are used to classify IT infrastructure elements, such as devices, servers, or applications, into different categories based on their characteristics and usage.

4. Clustering models: These models group IT infrastructure elements together based on their similarities, such as server clusters or network zones.

5. Optimization models: These models are used to optimize IT infrastructure resources, such as server allocation or network bandwidth, to achieve maximum efficiency and performance.

6. Simulation models: These models simulate IT infrastructure performance under different conditions, such as peak loads or system failures, to predict how the infrastructure will behave in real-world scenarios.

7. Control models: These models are used to control or automate IT infrastructure processes, such as server provisioning or application deployment, to ensure that they are performed consistently and efficiently.

8. Time series forecasting models: These models use historical time series data to predict future trends and patterns in IT infrastructure performance, such as server utilization or network traffic.

9. Fault detection and diagnosis models: These models are used to detect and diagnose faults in IT infrastructure components, such as servers or network devices, to identify potential issues before they become critical.

10. Capacity planning models: These models are used to plan and manage IT infrastructure capacity, such as server and storage capacity, to ensure that there is enough resources to meet the needs of the organization.

11. Change management models: These models are used to manage changes in IT infrastructure, such as software upgrades or hardware replacements, to minimize the impact on system performance and availability.

12. Network topology models: These models are used to model and visualize the network topology of IT infrastructure components, such as routers, switches, and firewalls, to ensure that the network is secure and efficient.

13. Performance evaluation models: These models are used to evaluate the performance of IT infrastructure components, such as servers or applications, to identify areas for improvement and optimization.

14. Risk assessment models: These models are used to assess the risk associated with IT infrastructure components, such as the risk of data loss or security breaches, to identify potential threats and vulnerabilities.

15. Machine learning-based intrusion detection models: These models use machine learning algorithms to identify and detect potential security threats and intrusion attempts in IT infrastructure, such as DDoS attacks or malware infections.

16. Resource allocation models: These models are used to allocate IT infrastructure resources, such as CPU cycles or memory, to different applications and processes based on their priority and importance.

17. Virtualization models: These models are used to manage and optimize virtualized IT infrastructure environments, such as virtual servers or virtual networks.

18. Disaster recovery models: These models are used to plan and prepare for IT infrastructure disasters, such as power outages or system failures, and ensure that critical systems and data can be restored quickly and efficiently.

19. Service level agreement (SLA) management models: These models are used to monitor and manage service level agreements between IT infrastructure providers and their customers, to ensure that agreed-upon service levels are being met.

20. Energy consumption models: These models are used to manage and optimize energy consumption in IT infrastructure, such as data centers, to reduce costs and environmental impact.

21. Security analytics models: These models use data analytics techniques to analyze and identify potential security threats in IT infrastructure, such as unusual network activity or unauthorized access attempts.

Client Engagement Duration: 3 Month to 6 Month.

No Matter whether you are on OnPrem or Cloud or in Hybrid

We will enable your team with AIOps solution no matter where your Infrastructure footprints are.

Zielbox AIOps Service Pricing

We are working on it and we will bring transparency soon on our Service Pricing Page so stay tuned.

Does Zielbox uses 3rd Party API or in house tools

At Zielbox, we prioritize cost-effectiveness and strive to minimize reliance on third-party solutions. While we recognize the value of certain third-party tools like OpenAI or Azure Copilot, we are conscious of spending and carefully evaluate their usage for our AIOps implementation.

We firmly believe that AIOps can be effectively achieved without relying on unsupervised learning. Instead, we leverage our expertise in supervised AI algorithms, which have already been configured and fine-tuned. These algorithms are implemented through our in-house codebase, allowing your team to modify and adapt them to meet your specific requirements, all without incurring additional costs.

We have found that utilizing OpenAI systems can be prohibitively expensive for AIOps work, especially when handling large volumes of logs and metric data. Our focus is on maintaining a balanced approach to spending on operation management, ensuring that the costs associated with third-party solutions are justified by the direct business value they provide.

By leveraging our in-house tools and solutions, we can provide you with a cost-effective AIOps implementation that effectively manages your operational needs while optimizing spending.