Comprehensive data + curation and grading + AI = powerful insights
Explore how signals are curated into actionable insights and correlated with diagnostic themes and workloads. Watch example diagnostic sessions for common IT scenarios to see how AI analyzes curated data to deliver precise root-cause insights.
- what new failures are happening this week we haven't seen before? - strange IO slowdowns after patch tuesday. why? who's affected? - we just fixed VPN flakiness on the lakeview ts. anyone else affected? - bob's account got hacked at 3:43pm. audit everything. impact?
Any question, any scope
One host, one client, or the whole fleet
Logs plus deep system state
CPU, RAM, IO and disk trends, projected forward. Snapshots plus instant deltas: services and VSS, BitLocker, leaks and blue screens, patch and servicing state, installs, event logs
Fleet-wide patterns before they become tickets
Spot issues emerging across clients from curated endpoint data
From question to root-cause and ticket-ready report
- ticket 7623 is off track. dig in and write it up - backups failed on fs-02 three nights running. why? - why is lakeview file server super slow every afternoon? - why did dc-01 at northridge reboot at 2am last night?
/sparklogs-investigate writes the report
Your agent queries fleet evidence, follows what it finds, and returns a cited root-cause report in minutes
Works where you work
Claude, Microsoft Copilot, Cursor, Codex, or your own MCP agents
Open-source skills, cited reports
/sparklogs-investigate writes the ticket-ready report; click any claim to verify the evidence; then dive deep into likely root causes and fixes