Case Study · Call Quality Monitoring
From Under 5% to 100% Call QA Coverage in One Month
DF deployed a structured call quality management platform that replaced manual, sample-based auditing with full coverage across 10 campaigns. In its first month of production, the platform processed 2,500 calls and recovered 100 hours of QA effort.
The Customer
A large BPO (business process outsourcing) company running high-volume inbound and outbound call center operations on behalf of its clients. At any given time its QA teams support 7 to 10 client accounts, each with its own scoring rubric, service standards, and compliance requirements.
The Problem
Every call is expected to be audited against a defined rubric, but with QA teams doing this manually, only a small fraction of calls were ever reviewed. The rest went unaudited, leaving quality gaps, inconsistent feedback, and no reliable way to coach agents or demonstrate compliance. The company engaged DF to build a platform that could bring structure and scale to the QA process: supporting multiple clients, standardizing how calls are scored, and giving QA teams the workflows they needed to actually do their jobs without the volume becoming a bottleneck.
The Solution
DF built a structured call quality management platform that replaced manual, sample-based auditing with full AI and human coverage across all 10 campaigns.
Unified call queue
Every call, across every client campaign, in one queue with live scoring and sign-off status.
Automatic AI scoring
Every call scored against the client's full rubric automatically, criterion by criterion, with a sentiment timeline and a pass/fail result.
Manual QA review
A structured workspace for QA specialists to score calls manually against the same rubric, synced to the transcript, for the calls that need human judgment.
Dispute and escalation workflow
Agents can dispute a score directly on the criterion in question. Disputes route into a queue with SLA tracking instead of getting lost in email threads.
Agent self-service
Agents see their own call history, scores, and sign-off status in a personal dashboard, without waiting on a QA manager.
Multi-account management
QA teams switch between client accounts and campaigns from a single workspace, so one platform supports all 10 campaigns.
See It In Action
One workspace for AI scoring, human review, and disputes.
Every call across every campaign in one queue, with scoring and sign-off status at a glance.
AI analysis scores every call against the rubric automatically, criterion by criterion.
QA teams review and score calls manually where human judgment is required, against the same rubric.
Disputed scores route into a clear escalation workflow instead of getting lost in email threads.
The Challenges We Faced
Moving from under 5% manual sampling to 100% AI-reviewed coverage raised its own questions. Scoring rubrics differ by client and campaign, so the AI analysis had to apply the correct rubric automatically rather than a single generic model. Agents and QA teams also needed to trust the AI's scores, which meant giving agents a clear way to dispute a result and giving QA leads a fast escalation path instead of a backlog of arguments over email. And because the platform now surfaces every call instead of a small sample, disputes and edge cases had to be resolved quickly enough that 100% coverage did not simply turn into a much larger backlog for the QA team.
The Results
The platform went into production in September 2026.
Early numbers from the first month of production:
10
Campaigns live across 7-10 clients
The platform supports multiple client campaigns from a single, standardized QA workflow.
2,500
Calls processed
Every call in scope was processed through the platform in the first month of production.
203 hrs
Of call audio reviewed
Roughly 12,000 minutes of call audio reviewed through the platform.
<5% → 100%
QA coverage
Coverage moved from under 5% of interactions to 100% of all interactions.
100 hrs
Of manual QA effort recovered
An estimated figure, conservative because it still accounts for calls where scores are under dispute and require human review.
Get In Touch