AI Observability

Gain actionable insights from cross-domain data ingestion & analytics

A Single Pane of Glass Over Your Technology Stack

AI Observability provides visibility over your entire tech stack, covering network, infrastructure, databases, storage, applications, and business services. Aisera supports integrations with popular systems for logs, metrics, traces, alerts, APM, DevOps, and ticketing such as ServiceNow, Salesforce, Jira, DataDog, Splunk, NewRelic, and more.

Proactively Detect Major Incidents & Performance Issues

Aisera predicts major incidents and identifies hard-to-find performance issues, which allows customers to take remediative actions and notify users.

AIOps and observability

Increase SRE and DevOps Productivity

Effectively triage issues with Aisera’s AI Causal Graph and pinpoint the root causes of issues faster with AI-driven root cause generation.

Domain Agnostic AIOps

AI Observability connects siloed monitoring systems, and reduces cognitive burden for your operations team by correlating incidents and alerts across your tech stack.

Major Incident Clustering

With Major Incident Clustering, you can group metrics, events, logs, traces, alerts, incidents, problems, and change requests to automatically discover meaningful clusters of data. AIOps incrementally learns from new data and from user feedback.

Impact and Root Cause Analysis

Identify the root cause of outages, as well as who will be affected by a major incident, with Aisera’s causal graph. Reduce mean time to resolution and increase customer satisfaction by proactively notifying customers of an outage.

Outage Insights

Predict performance and categorize issues to streamline your IT operations. Outage insights can be categorized as Early Warning, Performance Degradation and Service Outage depending on their severity level.

Intelligent Alerts

Cut out the noise with Aisera’s advanced event/alert deduplication, aggregation, and correlation. Using advanced AI/ML and Natural Language Processing (NLP), AIOps platforms drastically reduce the sheer volume of metrics, logs, events, traces, and alerts while pinpointing the handful that matters.