AIOps simplifies and automates IT operations
Improve the experience of your digital users by making the work of your operation teams easier and save costs thanks to Artificial Intelligence.
The impact that Digital Transformation is having on the technology of organizations makes it more relevant than ever for businesses. As technology users, we take for granted that all services will be functioning without interruption and without errors, and this makes the work of Systems Operation teams more complex as they face new challenges:
- The rapid growth in the volume of data generated by computer systems, networks and applications.
- IT infrastructure is evolving into very complex models with the increasing use of new technologies (unmanaged cloud, SaaS integration, etc.) that sometimes must coexist with legacy system.
- Increased variety of data with the need to analyse events, measurements, periodic data, etc.
- The need to reduce the operating costs associated with the execution of repetitive tasks where the responsibility of the operators in charge is not clear enough, which sometimes leads to non-compliance of SLA.
The use of Artificial Intelligence to analyse and manage the behaviour of these systems becomes a critical need to achieve proper system management, improving the work of IT teams that will be able to dedicate human talent to high-value tasks while intelligent systems keep infrastructure running safely.
Predictive technology, advanced dashboards, intelligent task automation, ticket management with Natural Language Processing, among other Artificial Intelligence techniques, are powerful tools that help optimize system operation while saving costs.
From ticket enrichment to automation
Laying the groundwork for proactive management of IT operations
Data Quality Assessment – Connecting to incident management systems, we apply advanced NLP to determine if information is missing before further processing.
Automatic ticket categorization – we use Machine Learning to create standardized catalogues for incident registration that allow you to classify tickets based on categories, automatically identifying keywords and drawing graphs that show the relationships between the groups of words obtained.
Inefficient reassignment analysis – The combination of the rule engine and set classifier can be used to predict which resolution group can better understand and resolve the problem, avoiding ticket bounce to improve overall resolution times.
Intelligent support group assignment – AI can be a great help in reducing the time spent on prioritizing and assigning tickets, and ticket information in correlation with the system status and workload of the real-time operating team.
Automatic Resolution Recommendation – This module analyses the semantic footprint of tickets and identify similar incidents regarding their resolution information to provide operations teams with guidance on the steps taken to resolve a similar incident in the past, thus streamlining resolution times and minimizing human error.
Temporary provisioning of incidents and tickets – using historical data from a ticketing system extraction, we can create a predictive model that will help the operational team to be the correct size before serious problems occur.
Detection of anomaly behaviours – Detect anomaly behaviours in a metric or set of metrics and generate an alert. Metrics can be technical (disk usage, CPU, memory, file system) or applications.
The use of automatic conversational interfaces introduces improvements to the workflow of different equipment. Conversational agents can act as problem-solving assistants through natural language conversations about different knowledge bases and monitoring systems, improving the efficiency of operating equipment.
Cognitive services for efficient IT operations management
Our methodology based on the latest techniques in Artificial Intelligence will allow you to get the most out of your data, tailored to your needs.
Customer expectations are higher than ever due to the rise of digitization. As users, we expect everything to run smoothly, and businesses need to deliver faster and more efficient support. Our experience during all these years supporting LARGE companies coupled with Inetum's vision of innovation, has allowed us to develop a differential value proposition on the use of AI applied to the operation of systems.
We work with market-reference technologies and create customized solutions to meet the needs of the most demanding operating teams, using our own set of tools as a basis:
- Procedures that accelerate the implementation of AIOps technology and procedures, simplifying the use of AI for organizations of any size, and creating a tailor-made experience to solve any use case.
- An AIOps methodology of its own with which to address projects of any complexity, adapting to the needs of our customers and ensuring their satisfaction.
- Ticket Enrichment algorithms and techniques with which we improve the management of ticketing systems using Natural Language Processing, understanding the needs of users and analysing how support teams work to offer specific solutions such as assigning to the best resolution group or identifying inefficient reallocations.
- A powerful kit of predictive algorithms with which to anticipate anomalies and correctly size IT operating equipment and services.
- AI-led automation capabilities that enable repetitive tasks to be efficient.
In addition to this toolkit, we provide specialized consulting services with an innovative and highly pragmatic approach through which we make our clients, regardless of their previous experience, aware of the possibilities offered by Artificial Intelligence applied in the day-to-day life of their IT operations, helping them to select the technology that best suits their needs within content costs and able to meet their expectations.
Rely on the experience of a large and innovative team to facilitate the use of Artificial Intelligence
Improve your users' digital experience and maximize the availability of their systems by anticipating problems and optimizing costs with AI. Help your IT Operations teams focus on what's important.
Artificial Intelligence in AIOps acts as a complementary digital workforce to human teams, who can focus their efforts on high-value-added work.
Through the use of AIOps technology we are able to improve the quality of the information that operational teams work with, increasing their productivity and ensuring compliance with service levels.
Artificial Intelligence's predictive capability improves the efficiency of different IT operating processes by preventively identifying anomalies and bottlenecks, allowing teams to anticipate and resolve situations that can lead to major problems.
Through intelligent self-discovering of IT assets related to business processes, we improve incident visibility and efficiently provision resources, saving time and costs.
The new predictive monitoring paradigms analyse dependencies and correlations using AI, identifying problems that depend on temporary events, thus improving resolution times by anticipating resolution, reducing recovery time.
Natural language processing for ticket enrichment ensures the best information processing, reducing operational costs and critical errors, and increasing the quality and availability of digital services.
Cognitive services and predictive technology for your IT operations
We create predictive models, real-time analytical systems, and cutting-edge technology to facilitate your entry into the new digital age.
The use of artificial intelligence in IT operations allows to automate the most critical parts of the process of attention of different requests, obtaining improvements in productivity and efficiency.
Through cognitive services it is easy to provide answers to frequently asked questions and provide adequate guidance for problem solving, thus simplifying and reducing incident analysis time.
Natural Language Processing (PLN) techniques allow you to extract relevant information (entities, suggested categories, etc.) from support tickets.
Thanks to a subsequent machine learning process (ML/DL), the intention that the user has when requesting the ticket can be determined, thus contributing to an enrichment of the initial request.
A business rule-based recommendations engine allows you to offer the most common resolution approach followed by previous iterations of the same or similar problem previously registered in the database.
Training predictive models using specific algorithms streamlines the definition of neural networks specialized in anomaly prediction and problem detection.
Event-driven architectures facilitate real-time analysis of all information from different analytical systems, probes, and knowledge databases.