Kllyroo's chat assistant calls real tools against your live data — server status, alert history, log search — instead of guessing from a prompt. Ask in plain language, get an answer grounded in what's actually happening right now.
A language model on its own doesn't know your infrastructure's current state — it can only guess, or worse, sound confident while guessing. Kllyroo's chat assistant instead calls actual tools against your live dashboard data: search servers by status, pull alert history, search logs. The answer comes from a live query, and the model's job is to phrase it clearly, not to invent it.
| Prompt | Tool call | What comes back |
|---|---|---|
| "Which servers are down right now?" | search_servers(status) | Live status, not a stale snapshot |
| "Why did server-13 alert this morning?" | get_alert_history(server) | The same causal reasoning shown in the incident view |
| "What's the root cause of the current incident on db-replica-2?" | get_incident(server) | Named root cause plus the effect chain |
| "Show me any recent errors mentioning connection timeouts" | search_logs(query) | Matching log lines with context |
223 root-cause signatures can't cover every possible log line from every possible stack. When a line looks worth attention but doesn't match a known pattern, it gets routed for an LLM-generated explanation instead of being silently dropped — so an unusual failure on an uncommon stack still gets some signal, even without a pre-written fix attached.
Offices in India and the US — reach out and we'll get back to you.