Support agents handle 50+ tickets/day. Each needs research (look up the order, check the policy), empathy (not sound robotic), and the answer. The mental switching cost is the worst part.
The AI approach
RAG-grounded drafting. LLM reads the ticket + customer history + retrieved policy snippets; drafts an empathetic, accurate reply; agent edits and sends.
The outcome
~+2.4× throughput
Agent throughput up ~2.4×. CSAT holds because the agent still owns voice + judgement.
Try itInput → Process → Output
Input — ticket + customer history
Ticket #4521customer@maxx-distribution · priority: med
Subject: "Order #88210 hasn't shipped — promised by Friday" "Hi — I placed Order #88210 last Monday for the new plant launch on Friday. Tracking still says 'preparing'. Can you tell me what's going on? We need this for production startup."
SLA: priority B · last issue resolved in 4h · loyalty tier: Gold
Process — AI pipeline
1Read ticket + historyReadGenerative
2Retrieve relevant policy (RAG)ReadPredictive
3Draft empathetic replyWriteGenerative
4Brand-voice + safety checkRulesSymbolic
Output — drafted reply
Click Run demo to draft a reply with empathy + answer + next steps.
Drafted reply RAG · 3 policy snippets
"Hi Khun Anan,
Thanks for the heads up — I can absolutely see why this is urgent with Friday's plant launch. I just looked into Order #88210: it cleared our warehouse yesterday but the courier (Kerry) has a 24h delay on the Bangkok-Rayong route this week.
Tracking will update later today. To make sure you're not stuck, I've also flagged this with our logistics manager to expedite the next leg — I'll get back to you with a confirmed delivery slot before EOD.
Apologies for the bump in the road, P'Ploy"
Grounded in: tracking record · Kerry SLA · escalation policy for Gold-tier customers. Brand-voice ✓ · No banned claims ✓ · Agent reviews + sends.
Three AI types in this use case
SymbolicBrand voice rules; escalation rules (refunds > ฿X to senior); required disclaimers; PII redaction in retrieved context.
PredictiveVector search over policy KB (semantic retrieval) + ticket-similarity match for past resolutions.
GenerativeLLM drafts the reply with the right blend of empathy, answer, next-step, and brand voice.