Support QA Review Hub

Your QA process shouldn't live in a spreadsheet.

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What changes when you build this

The gaps you're living with today,
and what this tool fixes.

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Problems
  • QA reviewers sample tickets manually from helpdesk exports, missing coverage on high-risk channels
  • Coaching notes are sent over Slack or email and never tied back to the original ticket
  • Agents don't know their QA scores until a weekly 1:1 — too late to change behavior that week
  • SLA breaches surface after the fact because no one is watching countdown timers in real time
  • CSAT dips go untracked until the monthly report, with no way to tie them to specific agents or ticket types
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Solutions
  • Ticket sampling is automated by channel, agent, and ticket type so coverage is consistent and auditable
  • QA scores and coaching notes are attached directly to the reviewed ticket — no separate tracking doc
  • Agents see their QA scores and flagged tickets in a personal scorecard updated after each review
  • SLA countdown timers are visible in the queue so reviewers can escalate before a breach, not after
  • CSAT trends are tracked per agent and per ticket category, updated daily instead of monthly

What the data model looks like

Refine generates this table structure from your
prompt. Edit columns, types, and relationships after.

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100%

Mistakes to avoid

These are the failure patterns teams hit most often
when building this.

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Inconsistent samplingFix: Define sampling rules by channel and ticket type so QA coverage is evenly distributed, not reviewer-dependent.
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Coaching notes that disappearFix: Attach all coaching notes directly to the reviewed ticket record so context is never lost in Slack or email.
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Agents blind to their scoresFix: Build agent-scoped scorecard views so reps see their QA results after each review, not at the next 1:1.
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SLA breaches discovered too lateFix: Add countdown timer columns to the review queue and flag tickets within 2 hours of breach.
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Score inflation from easy ticket selectionFix: Log which ticket types are being sampled and alert when reviewers consistently avoid high-complexity categories.

Frequently asked questions

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