Meet Kai. Your AI flags nineteen lakh alerts a quarter. Kai works them: evidence gathered, false positives closed, real ones escalated with a file.
2,340 alerts triaged this week. 2,114 drafted for closure with cited patterns, 226 escalated with case files. Median time-to-disposition: 41 minutes, down from 3 days.
What Kai does, item by item.
The whole queue, not a sample. Every finding cited back to its source.
Works every alert in the queue, oldest first, no cherry-picking.
Pulls the evidence around each alert before a human ever opens it.
Drafts the disposition: close with reasons, or escalate with a case file.
Learns your false-positive patterns and cites them, never silently applies them.
Keeps queue-time honest: no alert sits unworked past your SLA.
Writes the disposition trail your compliance review expects.
- ·The escalation call
- ·Regulatory filings
- ·Case decisions
- →Alert triage and evidence gathering
- →Draft dispositions
- →Queue SLA management
Kai closes nothing alone. Every disposition is a draft until a named analyst approves it, and the approval is logged with the evidence.
How it earns trust.
Pilot on your history, supervised on your queue, governed always.
Runs on a historical corpus your team already reviewed. You score its findings against what your reviewers caught. No production access.
Works the live queue. Every finding is a draft until a named human approves it, and every rejection is logged and measured.
The envelope never relaxes. Reading, checking, and drafting get automated. Approval stays human, by design.
The hand-off.
How Kai pings a human when it's your call.
The honest answers.
No dodging, no contact-sales-to-find-out.
Can Kai close an alert on his own?+
Our detection system already uses AI. Isn't this redundant?+
Does this satisfy compliance review?+
Put Kai on the queue.
Start with a paid pilot on your historical corpus, finished in weeks. Then a full deployment over 3 to 4 months, with a forward-deployed team working alongside your people.
