Tickets that investigate themselves.
When a chat escalates, ChatterMate opens a ticket and gets to work — forming hypotheses, querying your logs and metrics, and writing a root-cause analysis. You see every step, and you approve every action.
→ spike +412% at 14:02 UTC
→ isolated to stripe-eu region
→ 1,204 events · first seen 14:01
→ release v3.8.2
A timeout config in the stripe-eu adapter dropped from 30s to 3s, causing gateway timeouts for ~34 min. 1,204 checkouts affected.
Most AI tools hand you an answer. ChatterMate shows its work.
Every hypothesis, every query, every result is on screen — so your team trusts the conclusion instead of second-guessing it. Nothing that touches production happens without an explicit approval.
time-to-root-cause on escalated tickets, versus manual triage.
of investigation steps visible and auditable — no black boxes.
production actions taken without a human approving them first.
From escalation to root cause, on its own.
Opens the ticket
A chat escalates or an alert fires. A ticket opens with the full conversation, customer and product context attached.
Forms hypotheses
The agent ranks the likely causes by confidence and picks what to investigate first — all in the open.
Gathers evidence
It queries your logs, metrics and error tracking read-only, showing each query and its result inline.
Writes the RCA
It drafts a root-cause analysis, a customer reply and a fix plan — then waits for you to approve the action.
You set the leash.
Start investigate-only, then let the agent take low-risk actions on its own as trust builds. The AI never exceeds the level you pick — and every action is logged for audit.
Investigate only
Propose, human approves
Auto-resolve & notify
Whatever the level, gated actions — sending a reply, rolling back a release, refunding an order — wait for a click. Approve, or reject with a reason the agent uses to try again.
Not just the diagnosis — the follow-through.
SLA targets, customer updates and clean-up are built in, so a ticket runs itself from open to close.
SLA targets, per priority
First-response and resolution clocks the agent works against, so nothing quietly slips.
Keeps the customer posted
CSAT AFTER RESOLVETemplated ticket-created and ticket-resolved updates, sent from your own connected inbox — or ChatterMate’s address until you connect one.
Hi Northwind — we’ve opened ticket TKT-2038 and our team is on it. We’ll keep you posted.
Good news Northwind — TKT-2038 is resolved. Here’s what happened and how we fixed it.
Reads your tools. Never your customers’ chats.
Connect observability, databases and alerts as read-only sources with encrypted org keys. They attach only to the investigation agent — never to customer-facing chat.
Investigation connectors
VIA MCP · READ-ONLYRead-only access to your logs, metrics and errors so the agent can gather evidence — bring any platform with an MCP server: Splunk, New Relic, your own.
Verify facts in your database
Only SELECTs over tables you allowlist. Sensitive columns are masked before the AI sees them; every query is validated, row-limited and audited. The AI can never write.
Alerts open tickets on their own
Point Grafana, Datadog or CloudWatch alert webhooks here — an alert opens a ticket and kicks off an investigation before a customer reports the issue. Re-fired alerts attach to the open ticket.
Escalates to Jira, automatically
Tickets at or above a priority you choose also open a Jira issue in your connected project — tagged and linked back to the ticket. ChatterMate stays the source of truth.
ChatterMate’s AI ticketing turns an escalated chat or an alert into an open ticket, then investigates it in the open — ranking hypotheses, running read-only queries against your logs, metrics and database, and drafting a root-cause analysis. You choose the autonomy level per queue, and every production action waits for your approval with a full audit trail.
Frequently asked questions
What is AI ticketing in ChatterMate?
AI ticketing opens a support ticket the moment a chat escalates or an alert fires, then investigates it automatically — forming ranked hypotheses, querying your logs, metrics and database read-only, and drafting a root-cause analysis. Every step is visible and every production action needs your approval.
Can the AI make changes to production on its own?
No. Investigation is read-only, and any action that touches production — sending a reply, rolling back a release, refunding an order — is gated behind an explicit approval. You approve, or reject with a reason the agent uses to try again. Nothing runs without a human clicking through it.
How does ‘glass-box’ investigation differ from other AI support tools?
Most AI tools hand you an answer with no way to check it. ChatterMate shows its work: every hypothesis, every query and every result is on screen, so your team trusts the conclusion instead of second-guessing it.
What can the AI ticketing agent connect to?
It connects to observability and error tools via MCP (Grafana, Datadog, CloudWatch, Sentry, Elasticsearch and any MCP server), to your database as read-only allowlisted SELECTs with sensitive columns masked, to alert webhooks that open tickets proactively, and to Jira for one-way escalation of high-priority tickets.
Do I control how autonomous the agent is?
Yes. You set an autonomy level per queue — investigate-only, propose-and-approve, or auto-resolve for well-scoped low-risk queues. The agent never exceeds the level you pick, and everything it does is logged for audit.
Let your tickets do the digging.
Turn on AI ticketing and watch the first investigation run itself — with you in control the whole way.