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ServiceManager

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Problems

Root-cause analysis, a known error database, and proactive detection of recurring incidents before they become a pattern.

Problem investigation board in kanban view

Product screenshot coming soon

Investigation and lifecycle

Problems move through their own lifecycle, tracked visually so investigators can see where every open problem stands.

Explicit lifecycle states

Logged, Under Investigation, Known Error, Resolved, and Closed — a lifecycle distinct from incidents.

Kanban investigation board

Drag problems across investigation stages for a visual view of what the team is actively working.

Many-to-many incident linking

Link any number of incidents to a problem and any number of problems to an incident, reflecting how real outages relate to root causes.

Root-cause analysis

Structured templates keep root-cause analysis consistent instead of a free-text paragraph nobody reads later.

5-Whys template

Step through a structured 5-Whys template attached directly to the problem record.

Ishikawa / fishbone scaffolding

Capture contributing factors across categories using fishbone-style scaffolding for more complex root causes.

Structured root-cause analysis template on a problem record

Product screenshot coming soon

Known error database

Once a root cause is understood, promote it into a known error so agents can recognize it instantly next time.

Promote to known error

A problem with an identified workaround can be promoted into the known error database in one step.

Bidirectional knowledge links

Known errors link back and forth to knowledge base articles, keeping workaround guidance discoverable from both directions.

Known error database entry linked to a knowledge article

Product screenshot coming soon

Proactive trend detection

Recurring incidents don't have to wait for someone to notice the pattern manually.

Rules-based clustering

Recurring incidents are clustered by category, configuration item, and time window to surface patterns automatically.

One-click problem candidates

Clusters that look like a real pattern are suggested as problem candidates an agent can accept with one click.

Related

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