
Customer support
حوالہ جاتی نمونہFirst response in under a minute, without a confidently wrong answer
A support agent that resolves routine tickets end to end through narrowly scoped tools, states its confidence before acting, and escalates rather than guessing.
ایک نظر میں
- مدت
- 17 ہفتے
- ٹیم کا حجم
- 1 فرد
- معاہدے کی نوعیت
- نئی تعمیر
- پروجیکٹ کی قسم
- اے آئی اور آٹومیشن
- شعبہ
- B2B SaaS
کن چیزوں سے بنا
صورتحال
مسئلہ
Routine tickets — order status, refunds, password resets — consume the same triage attention as complex ones. But an agent that is occasionally confidently wrong damages customer trust more than a slow human queue does, so autonomy had to be earned rather than assumed.
ہم نے کیا کیا
Narrow the tools, enforce the limits in code, and make escalation the default whenever confidence is low.
وہ فیصلے جو اہم تھے
Tool-scoped access, not system access
Order lookup, account lookup, refund issuance with a hard cap, ticket updates, escalation — each validated in code independent of the model's reasoning. The limits are not instructions the model can talk itself out of.
Confidence stated before consequences
The agent declares confidence before any consequential action, and anything below a calibrated threshold escalates automatically instead of proceeding.
کیا بدلا
- of tickets auto-resolved
- 68%of tickets auto-resolved
- first-response time
- <60sfirst-response time
- escalation accuracy
- 96%escalation accuracy
- customer satisfaction
- +22%customer satisfaction
- 68% — no human involvement
- <60s — down from over four hours
- +22% — post-rollout
68% of tickets resolved without a human, first response under a minute instead of four hours, 96% escalation accuracy and a 22-point satisfaction improvement.
استعمال شدہ خدمات
- Tool-scoped support agent with code-enforced limits
- Confidence-based escalation and human handoff
- Full reasoning and tool-call audit trail
ہم کیا مختلف کرتے
ہر منصوبے میں ایسی ایک بات ہوتی ہے۔ اسے شائع کرنا ہی اصل نکتہ ہے — جس کیس اسٹڈی میں کوئی پچھتاوا نہ ہو وہ ثبوت نہیں، تشہیر ہے۔
The valuable engineering was not the reasoning quality, it was the tool design and the hard limits enforced outside the model. Starting with conservative thresholds and relaxing them against real evaluation data is what made it deployable.
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