QA sampling frameworks
Documented sampling percentage per queue, per shift, per channel.
Documented QA on every queue — sampling, scoring, error tracking and coaching feedback, with weekly QA reports you can audit.
Support quality is easy to claim and hard to prove. Most teams have a QA program on paper — sampling percentages, scorecards, review meetings — but very few can actually show you what quality looked like last month, by queue, with errors and corrections tracked.
Opervics runs customer support QA as a documented, auditable function: sampling frameworks on every queue, scoring against your standards, error tracking with correction turnaround, and a coaching loop that feeds findings back into the process.
Documented sampling percentage per queue, per shift, per channel.
Scoring frameworks aligned to your tone, policy and quality bar.
Error rates, correction turnaround, recurring patterns.
Findings fed back to agents and into runbooks and SOPs.
AI accelerates review; humans score, coach and decide.
Error rates, correction turnaround, coaching impact — auditable.
These are the same operational issues we see across clients — and the same ones our process is designed to remove.
Sampling percentages exist on paper but aren't actually performed.
QA reports show scores but not what to fix or where errors cluster.
Coaching happens, but not tied to specific findings or trends.
Errors logged but nobody confirms they were corrected.
QA findings don't feed back into runbooks, SOPs, or training.
QA activity can't be independently reviewed or audited.
Every service inherits the same baseline security posture, then adds service-specific controls on top.
Encryption in transit and at rest, role-based access, and least-privilege workflows across every account.
NDA-bound teams, restricted environments, and auditable access logs for regulated services.
AI output is reviewed by trained specialists before it reaches your systems — no blind automation.
Service-specific SLAs, tracking, and audit-ready reporting from day one.
Yes. QA stands alone as a measurable function — we can run QA on your in-house team's output, or on a mixed in-house + Opervics team.
AI accelerates review by flagging conversations for attention and surfacing patterns. Humans still score, coach and decide — AI doesn't replace QA judgment.
Sampling percentage is agreed in the scope — typically a documented percentage per queue, per agent, per shift. Some critical queues get 100% review.
Sampling coverage, error rates by category, correction turnaround, recurring patterns, coaching activity, and trend data — reported weekly.
Yes. Every QA cycle feeds findings back into runbooks, SOPs, training and process design — so quality improves over time, not just gets measured.
Per queue, per FTE, or as a monthly retainer — agreed upfront with no hidden add-ons.
A short conversation is usually enough to scope the process and propose a starting point.