Listing QA
Listings checked for content, attributes and accuracy.
QA on listings, tickets and storefront workflows — sampling, error tracking and correction turnaround — with AI-assisted QA that catches issues before customers see them.
E-commerce quality is easy to assume and hard to prove. Listings go live with errors, tickets get sent without review, storefront issues go unnoticed until customers report them. Every unnoticed issue is a conversion, a return, or a lost customer.
Opervics runs e-commerce QA as a managed function — listing QA, ticket QA, error tracking and correction turnaround — with AI-assisted checks that surface issues faster and human QA that scores and coaches.
Listings checked for content, attributes and accuracy.
Support interactions sampled and scored against standards.
Errors categorised, tracked and followed to correction.
Time-to-correction tracked and reported.
AI accelerates review; humans score and coach.
QA coverage, error rates and correction performance.
These are the same operational issues we see across clients — and the same ones our process is designed to remove.
Wrong prices, wrong attributes, missing content.
Support quality varies widely by agent.
Errors logged but nobody confirms they were corrected.
QA findings don't feed back into training.
Nobody knows QA coverage or error rates.
QA activity can't be independently reviewed.
Every service inherits the same baseline security posture, then adds service-specific controls on top.
Role-based access, least-privilege workflows, auditable logs.
Documented controls and segregation of duties.
Humans score and coach — AI doesn't auto-decide.
Coverage, error rates and correction performance reported weekly.
Listings, support tickets, storefront workflows and order accuracy — with scope documented per engagement.
Yes — QA works on your in-house team's output or on a mixed team, without requiring other services to be outsourced.
Sampling percentage is agreed in the scope — typically a documented percentage per queue, per contributor, per shift.
No. AI accelerates review and surfaces patterns; trained humans score, coach and decide.
QA coverage, error rates and correction performance — reported weekly.
Per SKU, per ticket, per FTE, or as a monthly retainer — agreed upfront.
A short conversation is usually enough to scope the process and propose a starting point.