TL;DR:
- This guide covers what AQM requires technically, where legacy STT infrastructure fails, and how to build a compliant, multilingual pipeline that holds up in production.
- Manual QA reviews only a small fraction of interactions through sampling, leaving most calls unanalyzed , a persistent compliance and coaching blind spot.
- AQM closes that gap by analyzing 100% of calls, but every downstream score, sentiment flag, and compliance check relies entirely on transcript quality.
- A weak STT layer produces wrong answers at scale. Transcription errors on accented speech or noisy audio pass silently through your QA pipeline undetected.
- Gladia's Solaria-1 achieves up to 29% lower WER than alternatives on conversational speech.
Core components of contact center QA
Quality assurance in a contact center measures whether customer interactions meet defined performance, compliance, and experience standards. When it works, you know whether agents follow scripts, whether customers leave satisfied, and whether your organization stays inside regulatory boundaries.
Building blocks of call center QA
Automated Quality Management (AQM) analyzes 100% of customer interactions using AI and speech-to-text infrastructure, scoring agent performance, tracking compliance, and extracting structured insights automatically. It replaces the manual sampling model with continuous, objective measurement across every call.
The four core functions AQM must cover are:
- Evaluation and monitoring: Voice interactions get transcribed into text, then processed through speech and text analytics to generate scorecards and performance metrics.
- Improvement: Automated scorecards surface coaching opportunities within hours, replacing the weeks-long manual feedback cycle.
- Customer experience enhancement: Analyzing what customers actually say across every interaction reveals satisfaction drivers and friction points that limited sampling approaches cannot reliably surface.
- Compliance: AQM systems automatically flag interactions that deviate from required scripts or regulatory requirements, creating audit trails and enabling proactive risk mitigation before violations compound.
Driving CX with quality monitoring
The link between QA coverage and customer satisfaction is direct. When agents receive faster, more accurate feedback tied to real customer interactions, they adjust behaviors faster. When QA covers 100% of calls rather than a small sample, you catch edge cases, accent-specific accuracy gaps, and compliance misses that never surface in a sampled review.
Ensuring data privacy and security
Call center audio is regulated data. Before you build or buy any AQM infrastructure, account for these compliance frameworks:
- TCPA (Telephone Consumer Protection Act): Applies specifically to contact centers running outbound marketing campaigns. Requires documented consent for automated dialing, prerecorded messages, and marketing calls. Does not apply to all contact center operations.
- DNC (Do Not Call Registry): Relevant to outbound telemarketing operations only. Makes it illegal for most telemarketers to call numbers on the registry. Inbound-only or non-marketing contact centers are generally outside its scope.
- GDPR: For any contact center serving EU customers, governs how personal data in recordings must be handled and gives customers control over their information.
- HIPAA: Governs how sensitive patient health information is handled in healthcare contact center recordings.
For product leaders evaluating STT infrastructure, the compliance question is whether your vendor's data handling defaults put your enterprise customers at risk. We hold:
- SOC 2 Type II certification: Verifies security controls held up operationally over a six-month period, not just on paper at implementation.
- ISO 27001: The international standard for information security management.
- HIPAA compliance: Covering healthcare contact center recordings involving patient data.
- GDPR compliance: Governing personal data handling for any call center serving EU customers.
Full details at our compliance hub.
Human QA: scaling and accuracy challenges
Manual QA worked when call volumes were manageable and regulatory requirements were simpler. Neither condition holds in modern contact centers. The problems with human review create structural blind spots that compound over time.
Unsampled calls hide performance gaps
Manual QA teams typically score only a small fraction of calls per agent each month through random sampling, with the exact number varying widely by team size and contact center policy. Contact centers review only a small fraction of interactions, leaving the majority unanalyzed and containing compliance violations, churn signals, and coaching opportunities that no one acts on.
Inconsistent scoring across evaluators
Human QA evaluators apply subjective judgment, and that subjectivity varies by reviewer, time of day, and call content. Two evaluators scoring the same call often disagree on whether an agent showed "adequate empathy" or properly completed a required disclosure. This inconsistency makes it impossible to build reliable performance baselines or to benchmarked coaching effectiveness over time. Automated scoring applies the same rubric to every call, which makes trend analysis and agent improvement tracking actually meaningful.
Weeks-long feedback loops slow improvement
Manual QA teams detect violations and coaching opportunities only when a specific call gets sampled, which can be weeks after the event. By the time an agent receives feedback on a poor interaction, the behavioral context is gone and the customer impact is already downstream. AQM processing returns a structured transcript within minutes of call completion, enabling same-day coaching while the interaction is still fresh for the agent.
High costs of scaling traditional QA
The unit economics of human QA don't hold at contact center scale. The average QA analyst in the US earns approximately $58,200 per year, roughly $28 per hour before overhead.
Key components of automated QA systems
AQM is a pipeline, not a single product. Each layer depends on the accuracy of the layer beneath it, which means your STT infrastructure sets the ceiling for everything else.
First step: transcribing calls for QA
STT is the foundation of every AQM workflow. Every downstream analysis, from sentiment scoring to compliance flagging to agent coaching, runs on the transcript. Transcript accuracy is not just a technical detail. It's the single variable that determines whether your QA pipeline produces reliable signal or expensive noise.
Our async API returns a structured JSON response in a single call, full transcript, word-level timestamps, speaker labels, named entity tags, and text-based sentiment scores with no additional API calls or separate enrichment vendors required. For the CCaaS use cases we support, this workflow consolidates what might otherwise require multiple vendor integrations.
Immediate vs. delayed QA feedback
Async transcription is the right workflow for post-call QA. Our async API processes approximately 10 minutes of audio in under one minute, meaning call transcripts with full diarization, sentiment, and entity extraction are available shortly after the call ends.
For most QA workflows, this delay is operationally irrelevant. Supervisors review the morning's calls before the afternoon shift. Coaching happens in weekly sessions. Compliance reports run daily. None of these workflows require sub-second latency. They require accuracy, completeness, and structured output that routes cleanly to a dashboard or LLM scoring layer.
Eliminate blind spots: full QA coverage
Moving from sample-based review to 100% coverage changes the quality of every compliance claim, every performance report, and every coaching decision. With sampling, you report on the calls you reviewed. With full coverage, you report on what actually happens across all interactions.
For regulated industries, this distinction matters during audits. "We analyzed 100% of calls and found a 94% compliance rate" is a verifiable fact. "We sampled 3% of calls and found 94% compliance in that sample" is a statistical projection, one whose reliability depends entirely on whether that sample was representative, and whose confidence intervals widen as call volume and linguistic diversity increase.
Most legacy STT APIs were built and tested on clean, accent-free American English. Accuracy can degrade when call centers handle diverse linguistic environments, including code-switching scenarios common in multilingual markets. The degradation isn't always obvious: you don't get an error message. You get a plausible-looking but inaccurate transcript that passes through your QA pipeline undetected.
Essential QA metrics for call centers
Once the transcription layer is reliable, you can build QA scoring around the metrics that actually matter for agent performance, compliance, and customer experience.
Policy and script adherence QA
Compliance QA tracks whether agents deliver required disclosures, follow mandated scripts, and avoid prohibited language. For TCPA compliance, that means verifying consent language on recorded calls. For debt collection, it means confirming agents stay within regulatory boundaries. For healthcare contact centers, it means ensuring HIPAA-required disclosures appear on every relevant interaction.
AQM handles this through keyword and phrase detection on the structured transcript, automatically flagging any call where required language is absent or prohibited language appears. The audit trail attaches to a timestamped transcript and requires no human reviewer to catch it.
Detecting customer dissatisfaction early
Text-based sentiment analysis runs NLP models against the transcript to infer customer emotional state from what they said. Sentiment models flag dissatisfaction indicators such as repeated issue statements, negative polarity phrases ("frustrated," "unacceptable," "cancel"), and escalation language ("speak to your manager," "file a complaint"). We provide text-based sentiment inference on transcripts.
QA teams can operationalize sentiment scores by routing calls below a defined threshold. For example, sentiment polarity < -0.5 - to a human reviewer queue for follow-up or escalation prevention. Sentiment trends across agent cohorts surface whether dissatisfaction signals cluster around specific agents (individual coaching opportunities) or appear system-wide (product issues, script failures, or policy gaps that require broader intervention).
Sentiment scoring requires high-quality transcripts to produce reliable signal. Fixing WER at the infrastructure layer is what makes sentiment analysis worth running.
Agent tone, empathy, and delivery
Structured transcript outputs feed directly into LLM scoring layers that evaluate soft skills: whether an agent acknowledged a customer's frustration, whether they apologized appropriately, and whether their phrasing matched the required empathy rubric. This scoring requires transcripts that correctly separate agent speech from customer speech, which is why accurate speaker diarization is a prerequisite, not a nice-to-have.
Measuring first call resolution (FCR)
Named entity recognition (NER) and transcript summarization let you track whether the customer's stated issue appears resolved by the end of the call, whether a callback was scheduled, and whether the interaction escalated. Combining entity extraction across a customer's full call history gives you a programmatic FCR measurement that doesn't require a post-call survey.
How Gladia's async API supports call center QA
The bottleneck in most AQM pipelines isn't the scoring logic or the dashboard. It's the transcript quality feeding them. Here's where each layer of our async API contributes directly to QA outcomes.
Production-grade WER on noisy audio
We benchmark Solaria-1 against eight providers across seven datasets and 74+ hours of audio. On conversational speech, which is what call center audio actually sounds like, Solaria-1 achieves up to 29% lower WER than alternatives. On speaker attribution, it delivers up to 3x lower diarization error rate.
The downstream impact of WER differences compounds quickly. A single transcription error on a customer's name or account number corrupts a CRM entry. A mis-attributed speaker label flips a coaching score from the agent to the customer. A wrong sentiment polarity on a high-WER transcript means a frustrated customer gets scored as satisfied.
Speaker diarization for agent-customer separation
Diarization segments a multi-speaker audio recording by speaker identity, assigning each utterance to the correct participant. In a QA context, this is what makes it possible to score the agent separately from the customer, and to correctly attribute compliance language to the agent rather than a customer quotation.
We power diarization with pyannoteAI's Precision-2 model, available in async workflows. Speaker attribution errors don't stay isolated in the transcript: they affect summaries, CRM updates, QA workflows, and any analytics pipeline built on top of the conversation.
A structured diarization output from the async API looks like this:
{
"utterances": [
{
"speaker": "speaker_A",
"text": "I need to cancel my account immediately.",
"words": [
{"word": "I", "start": 0.5, "end": 0.7},
{"word": "need", "start": 0.7, "end": 0.9}
]
},
{
"speaker": "speaker_B",
"text": "I completely understand. Let me pull up your account.",
"words": [
{"word": "I", "start": 1.1, "end": 1.3}
]
}
]
}
This separation is what makes downstream QA scoring work. Every compliance check, sentiment score, and coaching flag attaches to the correct speaker.
100+ language support for call center QA
Solaria-1 covers 100+ languages, including 42 not available through any other STT API, which includes Tagalog for Philippine BPOs, Tamil and Bengali for Indian contact centers, and Spanish-English code-switching for US Hispanic markets.
Sub-24-hour integration
Our async API connects via REST with SDKs available for multiple programming languages. The recommended parameters documentation covers QA-specific configuration including diarization, NER, summarization, and custom vocabulary. Multiple customers independently report sub-24-hour integration from first API call to production.
Aircall cut transcription time by 95%, from 30 minutes to 1.5 minutes per call, and now processes over 1M calls per week through our API.
Fixed costs for scaling call centers
Our pricing model charges per hour of audio processed, with all audio intelligence features included in the base rate on Starter and Growth plans. No add-on charges for diarization, sentiment analysis, translation, or NER. The full tier structure is available on Gladia's pricing page.
Here's how unit economics look at scale:
| Volume |
Starter ($0.61/hr) |
Growth (from $0.20/hr) |
| 1,000 hours/month |
$610 |
From $200 |
| 10,000 hours/month |
$6,100 |
From $2,000 |
| 50,000 hours/month |
$30,500 |
From $10,000 |
The Starter tier includes diarization, sentiment, NER, and translation. On Growth and Enterprise plans, we never use customer audio to retrain models, with no opt-out action required from your side. On the Starter plan, data may be used for model training. If your enterprise customers have data governance requirements, paid plans are the correct starting point.
Implementing AI-powered QA pipelines
Building an AQM pipeline on top of reliable STT infrastructure follows a clear five-step sequence. Each step depends on the previous one being solid.
1. Map QA metrics to business goals
Before writing a single line of integration code, define what you're measuring and why. Good QA metrics tie directly to business outcomes: CSAT, FCR, compliance adherence rate, agent performance score, escalation rate. Map each internal QA criterion to the business outcome it predicts. This determines what you need to extract from transcripts and what your scoring rules need to evaluate.
2. Deploy STT for call QA
Choose an async-first API that handles diarization natively in the same call. The async workflow fits post-call QA best: it analyzes the full recording before producing output, which is well-suited for accurate speaker attribution and multilingual consistency.
The Gladia async transcription documentation covers how to configure diarization, NER, sentiment, and custom vocabulary in a single API call. For call center audio specifically, enabling custom vocabulary for your product names, agent codes, and compliance terminology can help improve transcription accuracy on domain-specific terms.
3. Design precise automated scoring rules
Route structured JSON output from the STT layer to your QA scoring logic or LLM. Scoring rules for compliance QA typically check for required phrase presence, prohibited phrase detection, and required disclosure sequencing. For agent performance scoring, you run sentiment and entity extraction against the agent's utterances specifically, which requires reliable speaker diarization upstream. The multilingual transcription guide covers configuration considerations for global call center deployments.
4. Real-time QA dashboards and alerts
Aggregate transcript, diarization, sentiment, and compliance data into dashboards that supervisors and QA managers can act on. Prioritize flagging: not every call needs human review, but calls flagged for compliance deviations, extreme negative sentiment, or low QA scores should route to a human reviewer queue automatically.
Close the feedback loop. Coaching that references specific call timestamps tied to the transcript is more actionable than coaching based on a supervisor's recollection. With AQM and async transcription, every call is available for coaching reference, not just the small fraction that happened to get sampled.
Tangible business outcomes of automated QA
The ROI case for AQM infrastructure is measurable across four dimensions that product leaders can model before committing to a build decision.
Eliminate sampling with full call QA
Switching to full-coverage AQM means every compliance claim, every performance report, and every coaching decision is grounded in what actually happened across all calls, not a statistical projection from a small sample. Manual QA teams detect violations only when a specific call gets sampled, which can be weeks after the event. Full AQM coverage means violations surface the same day they occur, making remediation faster and audit trails more complete.
Accelerated QA feedback loops
Manual QA with sampling-based review creates multi-week delays between a bad call and an agent coaching session. AQM processing in async batch mode returns structured transcripts within minutes of call completion. That compression directly enables same-day coaching while the interaction context is still fresh for the agent.
The comparison below shows what changes when you shift from manual sampling to full automated coverage:
|
Traditional QA |
Automated QA |
| Coverage |
Small sample of calls |
100% of calls |
| Speed to feedback |
Weeks |
Same day |
| Cost to scale |
Linear with headcount |
Usage-based ($0.61/hr Starter, from $0.20/hr Growth), all features included |
| Consistency |
Evaluator-dependent |
Rule-based criteria |
A well-instrumented AQM program that catches coaching opportunities quickly and maintains comprehensive compliance coverage can produce measurable CSAT improvements. When your QA pipeline covers more calls and flags more resolution opportunities, improvements can compound across the entire agent population rather than only the fraction that got reviewed.
Start with 10 free hours and test our async API on your own call center audio, including noisy environments, accented speech, and multilingual calls, before committing to an infrastructure decision.
FAQs
What word error rate should you target for production call center QA?
For call center QA, WER targets depend heavily on audio conditions and use case requirements. Clean recordings with standard scripts may achieve single-digit WER, while noisy multi-speaker environments with accents often see higher rates. For critical compliance applications in regulated industries like financial services and healthcare, error tolerance is much stricter. Gladia's Solaria-1 achieves up to 29% lower WER than alternatives on conversational speech per the async benchmark methodology, with production customers reporting strong performance in real-world conditions on their specific audio types.
Test your STT layer against audio samples from each target market, specifically with native-language speakers using regional accents, before expanding coverage. Our automatic language detection handles 100+ languages including true mid-conversation code-switching, which prevents the silent failures that typically appear as accuracy regressions only after customers complain.
Does AQM eliminate the need for human QA reviewers?
No. AQM shifts the human reviewer's role from sampling and manual scoring to exception review, nuanced compliance decisions, and coaching conversations, which is a more valuable use of their time that requires judgment AQM can't replace. Automated systems flag calls that need human attention and generate coaching materials tied to exact transcript timestamps.
On Growth and Enterprise plans, we never use customer audio to retrain models, with no opt-out action required from your side. The Starter plan may use data for training. Full compliance details including SOC 2 Type II, HIPAA, and GDPR documentation are available at the compliance hub. Dedicated deployment options are available for organizations with strict data residency requirements.
How quickly can an engineering team integrate Gladia for call center QA?
Multiple customers independently report sub-24-hour integration using the REST API and SDKs. The getting started documentation covers the full async transcription setup, including diarization, NER, and sentiment configuration in a single API call, with start with 10 free hours to test on your own call center audio.
Key terms glossary
Automated Quality Management (AQM): A QA approach that uses AI and speech-to-text infrastructure to analyze 100% of customer interactions, automatically scoring agent performance, compliance, and customer experience metrics. Also referred to as automated quality monitoring, AQM replaces manual sampling — typically covering only a small fraction of total interactions - with systematic, objective measurement across every call.
Word Error Rate (WER): A standard metric for measuring transcript accuracy, comparing the transcribed words to the actual spoken words. Lower WER means higher transcript accuracy and more reliable downstream QA signal.
Diarization Error Rate (DER): A metric measuring speaker attribution accuracy in a diarized transcript. Lower DER means more accurate speaker identification, which is essential for any downstream QA metric that depends on per-speaker analysis.
Code-switching: The practice of alternating between two or more languages within a single conversation. Many STT systems struggle with code-switching, which can produce degraded transcripts that affect downstream QA scoring.
Diarization: The process of segmenting a multi-speaker audio recording by speaker identity, assigning each word or utterance to the correct participant. In call center QA, accurate diarization makes it possible to score agent speech separately from customer speech. We power Gladia's diarization with pyannoteAI's Precision-2 model in async workflows.
