Overview
Today, your remediation team is forced to take a painstaking Trial-and-Error Approach to resolve incidents. It is extremely time-consuming and doesn’t lend itself to more effective and predictable outcomes via systematic learning. FogLogic combines AI/ML with collaboration technologies to remediate incidents proactively. The collaboration content is mined to enable closed-loop learning, ensuring that information about what worked and what didn’t informs the knowledge base so that automated recommendations improve over time. At the same time, this builds a foundation for a future “self-healing” system.
Incident Contextualization Business Drivers
Streamlined business processes
across team and system silos
Institutional
knowledge building
Stakeholder satisfaction
with service levels
Key AIOps Capabilities & Features
Rules-driven Mobilization
- Automates policies for triggering and routing remediation team activity
- Integrates with popular ITSM systems such as ServiceNow®
Remediation Workflow Features
- Standardizes communication across teams for tasks like applying, testing, and reporting on fixes
- Leverages (FogLogic LiveLink™) integrations with Slack and Microsoft Teams so resolution teams can work across system silos and across time zones
Closed-loop Learning
- Ensures that information about what worked and what didn’t for a given problem are fed back into the knowledge base so that automated recommendations improve over time.
- Automates capture and indexing of insights and learnings that are used to improve upstream process such as dynamic anomaly detection thresholds, root cause analysis and automated recommendations, as well as remediation action outcomes
- Streamlines remediation and learning processes leveraging bi-directional troubleshooting tool integrations