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Customer support

نموذج مرجعي

First 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.

<60sfirst-response time

لمحة سريعة

المدة
17 أسبوعاً
حجم الفريق
1 شخص
نوع التعاقد
بناء جديد
نوع المشروع
ذكاء اصطناعي وأتمتة
القطاع
B2B SaaS

الوضع

التحدي

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.

ما الذي فعلناه

Narrow the tools, enforce the limits in code, and make escalation the default whenever confidence is low.

القرارات التي صنعت الفارق

  • 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.

ما الذي تغيّر

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.

الخدمات المستخدمة

  • Tool-scoped support agent with code-enforced limits
  • Confidence-based escalation and human handoff
  • Full reasoning and tool-call audit trail

ما الذي كنا سنفعله بشكل مختلف

لكل مشروع واحدة من هذه. ونشرها هو المقصد — فدراسة حالة بلا ندم فيها تسويق لا دليل.

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.

الخدمات المستخدمة

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