AI AppAnalyst turns AppLogic’s deep network and application intelligence into natural-language investigation—so teams can find issues faster, explain them clearly, and resolve them confidently.
Modern networks are increasingly complex: encrypted traffic, dynamic applications, and constantly shifting demand create more incidents—while teams are expected to move faster with the same headcount.
Most operations dashboards still require specialized expertise, manual correlation across tools, and time-consuming “needle-in-a-haystack” analysis. The result: slower isolation, more tickets, and less confidence in what actually changed.
AI AppAnalyst is a Layer 3 capability that sits above Operations Insights / Enterprise Insights and:
- Maps natural language questions into queries across AppLogic’s multidimensional data model
- Detects outliers and patterns across contextual dimensions - Runs deeper automated investigations
- Produces clear, GenAI explanations (plus tables/charts) with an audit trail of its reasoning
AI AppAnalyst can be used: 1) Directly (Q&A dashboard) 2) Inline from Insights dashboards via contextual drilldowns
AI AppAnalyst requires connectivity to a cloud-based LLM service.
Two privacy options (customer choice): - Option 1 — No data sent to the LLM: the LLM helps generate the query; results are returned and visualized without sending the data for narrative analysis. - Option 2 — Data sent to the LLM: results are sent for analysis and insights. By default, subscriber specifics are obfuscated to the LLM.
AI AppAnalyst is designed to sit alongside—and amplify—your Insights layers:
Operations Insights: customer-centric operations workflows and contextual dashboards
Enterprise Insights: 360° application- and user-centric observability
AI Labs: the broader innovation engine for agents, models, and outcome packs
Version note: Operations Insights must be on 27.40, with the rest of the lineup on 26.30 or above.
AI AppAnalyst is built for real operations:
Human-in-the-loop by default: teams review results before changes
Privacy controls: choose whether to share results for narrative analysis
Auditability: reasoning trail supports trust and learning
(For buyers who care about AI risk and data leakage: the page can optionally include a short note aligning to common best practices around AI risk governance and data leakage controls.)
