Collections quality management at Skit.ai is AI-driven . The Auditor Agent reviews every conversation against one fixed standard, and the Coach Agent turns each finding into the next agent's improvement. Quality stops being a monthly sample and becomes a closed loop, where every call is audited and every defect is coached.
SampleStep 1 of 5
Stratified random sampling draws a representative, statistically-sound set from every day’s conversations.
99%
Sampling confidence
±5%
Calibration variance
100%
Audit coverage
Dedicated QC Team
Coverage That Flexes to Where It's Needed
A dedicated quality analyst rides with every team. Monitoring isn't flat. Performers who are improving get audited more often, so attention follows risk.
Top performers
Monitored monthly
6calls / mo
Average performers
Monitored monthly
8calls / mo
Improving performers
Monitored monthly
10calls / mo
Tier 01
Top performers
6 calls / mo
Light-touch confirmation audits
Spot-check on critical-defect items
Best-practice capture for the team
Already at standard — 6 calls / month keeps them honest without slowing them down.
Quality Audit Framework
One Path From Sample to Fix
Every audited interaction travels the same five stages - stratified sampling through to closed-loop correction. Nothing is spot-checked at random; the framework is the control.
1Sampling & sample size
2Call / transaction monitoring
3Assemble & analysis
4Corrective & preventive action
5Escalation management
Audit pipeline · live
Stratified random sampling
A representative, statistically-sound draw — higher sampling where risk is higher. Checklist designed with critical items and weightages.
Interaction Analytics · Variation Control
The Audit Hears What People Miss
Analytics run across every medium, not just whether the agent passed, but how the conversation actually felt. Dead air, talk-over, and sentiment surface the variation a checklist can't.
Conversation waveform · analyzed
SpeechDead airTalk-over
Sentiment analysis
Tone tracked turn by turn. Frustration and de-escalation flagged, not guessed.
Dead air & talk-over
Silence and overlap quantified on every call as objective experience signals.
Process-gap detection
Where the conversation drifts from the standard, the gap is named, not buried.
Root-cause & common-cause
One-off agent slip or systemic process issue? RCA separates variation from common cause.
Quality Management Process
From a Single Call to a Better Operation
Audits don't end in a scorecard. Every finding flows from the agent's interaction into analysis, and back out as concrete improvement.
Source
Interaction captured
Every agent conversation is recorded and pulled from the production extract, nothing relies on memory.
AgentCall recordingProduction extract
Audit
Quality audit team
Analysts score against the checklist, log defects, and run root-cause analysis on what the audit surfaces.
ScoringDefect logRCA
Impact
Insight & action
Findings roll up into business trends, CX management, and improvement plans that change how the next call goes.
Business trendsCX managementImprovement plan
Measured to a Standard
Governed by Evidence, Not Spot Checks
99%
Sampling confidence
statistically sound
±5%
Calibration variance
client & internal
100%
Audit coverage
end-to-end
SRS
Stratified random sampling
method
1:1
QC analyst
dedicated per team
Quality you can prove, on every conversation.
Talk with our team about how Skit.ai runs collections quality management as a calibrated, closed-loop standard across your operation.
