The national telecommunications carrier of a small island nation.
A thousand customer chats a day, moved through the day instead of building.
Agents open each chat to a pre-built brief, not an empty draft.
Send the queue, inbox, or report with the clearest delay.
Mia maps the first similar Rebotify fix.
The national telecommunications carrier of a small island nation.
A thousand customer chats a day, moved through the day instead of building. Agents open each chat to a pre-built brief, not an empty draft.
Volume
~1,000 chats/day
Across the carrier’s primary channel
First-response time
Minutes → under 30 sec
Through the day, including peak
Autonomous resolution
60–70% containment
With re-contact rate held flat
Agent-side AHT
15–25% faster
On the cases that reach a human
A thousand customer chats a day across a small, dense market. Most of them were the same handful of questions — bill enquiries, prepaid top-ups, plan changes, roaming, SIM activations, outage check-ins. Each one looked routine until you’d answered it for the eighth time that morning.
Agents handled what they could during the day and the evening peak built into a queue that the overnight shift inherited. By the weekend the queue had texture — actual incidents tangled up with sixty repeat questions about the same evening’s mobile data top-up.
A chat triage officer.
Scoped to first-touch chats only. No billing changes, no service credits, no plan adjustments, no enterprise accounts.
Connections
- Customer account database (read-only)
- The chat platform
- Knowledge base and standard reply library
- Outage and incident status feed
- Escalation routing rules
- Read every incoming chat and classify against the top six categories
- Pull account context — plan, recent usage, last billing event, last service activity — before drafting
- For routine categories with a clear answer, draft the reply in house tone
- For service credits, complaints, enterprise accounts, or outage-impact compensation, route with one-line context
- Surface to the agent with the draft pre-loaded
The outage pattern got missed. During the first incident in week one, the employee was confidently answering “your service should be working from your side” to a cluster of customers whose service in fact wasn’t. The reply was technically correct against the account record and exactly wrong against reality.
The rule changed: any cluster of three or more similar-pattern messages within a ten-minute window triggers a hold on outbound drafts and a flag to the network operations queue. The outage status feed got wired into the employee’s context the same day.
- Billing adjustments, credits, refunds
- Plan and service changes
- Enterprise and B2B accounts
- Complaint resolution and any escalation
- Anything during an active outage — the wrong autonomous reassurance at the wrong moment is the worst-case interaction
- Reading the chat and classifying it
- Pulling account context before drafting
- Drafting the routine reply in the right voice
- Detecting outage patterns and pausing drafts
- Routing edge cases with reasoning attached
The deployment sits in the band published comparable telecom AI cases describe: first-response times under thirty seconds during peak, 60–70% of triaged chats resolved without an agent typing, and 15–25% faster handle time on the cases that do reach a human — with re-contact rate held flat as the honesty check on resolution.
- Touch billing. Adjustments, credits, refunds — all human.
- Change a plan or service. The employee can describe options; only an agent makes the change.
- Handle enterprise accounts. B2B routes immediately, no exceptions.
- Speak during an active outage. Outage-pattern detection holds drafts and routes to a human.
- Send without review. Every customer-facing draft passes through an agent.
Agents now open each chat to a pre-built brief — customer context, recent activity, draft reply already prepared for the routine categories, edge cases pulled into the right queue with a summary attached. The evening peak still happens — it always will — but it clears inside the evening instead of waiting for the next morning. The work that needed a person — the complaints, the outage conversations, the enterprise escalations — is the work agents are actually doing.
Chat #2814 — prepaid data top-up question, account in good standing, drafted from top-up template. Held for Aishath’s approval.
Cluster detected — 4 messages re: data signal in last 6 mins. Drafts on hold. Flagged to NOC. No outbound.
Other deployments on the same operating model.
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Send the queue, inbox, or report with the clearest delay.
Mia maps the first similar Rebotify fix.