Managing service assurance independently within each service layer constraints, operational productivity, and the service assurance process. Teams are often chasing symptoms within their silo when the root cause of the problem lies outside their visibility and control. Multiple operations support teams may be addressing the same or different symptoms but all of them are attributable to the same root cause. Addressing these service issues is slow and extremely labor-intensive.

VIA AIOps delivers the information required to manage faults and performance across the service delivery stack in one application with a single incident inbox.

VIA AIOps automatically determines the correct algorithm to use on streaming data to generate baselines.  Unsupervised machine learning generates baselines for every metric and dimension combination.

Baselines are created as data is ingested and continuously updated and improved over time with more data. The use of machine-learned baselines versus simple thresholds allows VIA to reduce noise and detect anomalous behavior occurring during both low usage and peak periods.

Baselines more accurately detect faults and performance issues. VIA AIOps delivers a 4X reduction in signals requiring actions as compared to competitive solutions.

With only a handful of out-of-the-box AI algorithms, you quickly gain the advantages of VIA’s deduplication, alarm noise reduction, and incident prioritization for all hosts, entities, or services in your operating environment.  Artificial Intelligence and machine learning embedded within VIA’s out-of-the-box AI algorithms are explainable, extensible, and easy to use.

Built for horizontal scaling, VIA AIOps reliably supports mission-critical operations that require massive data capabilities.

VIA simplifies the process of data ingestion through Signal Onboarding, a GUI-driven toolset that:

• analyzes sample data
• auto-generates a real-time parser
• models the data for real-time analysis.

Never-before-seen data feeds can be ingested in less than an hour without the need to write code. Data does not need to fit a specific data model or specifications, eliminating the development of thousands of lines of code to ingest and parse data sets. The user can onboard data from off-the-shelf and home-grown tools, new SNMP traps, bulkstats, Syslog, and other data sets in the field without any coding or feature requests.

VIA is based on an explainable UI and the users’ ability to adopt VIA to their operational best practices. This clear-box approach builds trust and instills confidence in operations teams to use analytics to further process automation.

VIA’s feedback loop combines human and artificial intelligence to continuously improve the accuracy of AI algorithms and support the enablement of automated actions.  VIA provides information on each incident it discovers including the probable root cause, key symptoms, impacted populations, duration, and severity.

Users can provide feedback and provide context.  The system uses this information for relevance ranking and training the algorithm for greater accuracy.  In addition, this information over time can be used to determine if automated action should be implemented based on the consistency of the response needed to respond to the incident.

To further maximize operational productivity, dashboards can be created for all users, from executives who need a high-level view of service performance to the service reliability engineers who need to act on the identified service performance issues.

VIA delivers explanatory AI, visual and text-based explanations of all analyses and actions. Users can drill into why a detection occurred, why a baseline was generated the way that it was, why signals were correlated, and why a root cause was identified.