The hardest part of AI performance monitoring isn't building the scorecard. It's getting a reliable transcript from noisy, accented, multilingual call center audio in the first place.
Most product teams underestimate this until they're debugging false QA flags generated from garbled transcripts of a bilingual support call in Manila or a heavily accented customer in Marseille. By then, the downstream LLM has already produced a compliance score that's technically wrong, and the QA manager is manually reviewing audio to figure out why.
Moving from manual spot-checking to 100% AI coverage requires more than dropping an LLM on top of your existing audio pipeline. It requires choosing the right integration architecture, anchored by speech infrastructure that handles noisy audio, code-switching, and strict data residency requirements without breaking your unit economics as call volume scales.
The integration process reduces to four moves:
- Capture every call using a recording layer that feeds raw audio to a transcription API.
- Transcribe with structured outputs, pulling diarized speaker turns, detected languages, sentiment signals, and named entities from each conversation.
- Score against defined criteria by routing the structured transcript to an LLM scorecard engine that evaluates script adherence, compliance language, and resolution quality.
- Trigger coaching workflows automatically when scores fall below thresholds, surfacing coachable moments to managers rather than raw call recordings.
Each step depends on the one before it. If the transcript misattributes a speaker or drops a key phrase, the scorecard is wrong and the coaching trigger fires on bad data.
How AI powers contact center QA
Reactive QA vs. proactive AI insights
Traditional QA is reactive by design. A supervisor typically samples a subset of calls, marks up a scorecard, and delivers feedback after the call occurred. By that point, the customer interaction has already concluded, and the coaching opportunity may be less timely.
AI monitoring flips this model. Instead of reviewing a curated sample after the fact, you can analyze every call shortly after it completes and flag issues for review. That gap is where compliance breaches hide and agent coaching opportunities expire.
The shift from manual sampling to 100% coverage
At high call volumes, even a small sampling percentage means reviewing only a fraction of interactions. An AI pipeline processing all calls means your QA team spends its time validating AI findings and acting on patterns, not listening to recordings. The Selectra case study illustrates the shift: their QA team now validates AI findings rather than manually reviewing audio from scratch.
The modern AI QA stack: core components
A production AI QA stack typically includes multiple layers working in sequence.
Consistent AI-powered QA scoring
The scoring layer takes structured transcript data and evaluates it against predefined criteria: did the agent use the required compliance disclosure, was the opening greeting correct, was the call resolved within the defined threshold. The engine applies the same criteria to every call without variation, eliminating the inconsistency that plagues human-only review.
Detect customer emotion accurately
Text-based sentiment analysis classifies each transcript segment as positive, negative, or neutral, then attributes those classifications to the specific speaker who produced them. One critical distinction: Gladia provides text-based sentiment analysis derived from the transcript, not acoustic emotion detection from vocal characteristics like pitch or tone. Text-based sentiment operates on lexical content, so it has limitations when the same words could convey different emotional states depending on delivery. Keep this in mind when defining what your scorecard can and can't reliably detect.
Safeguard against policy breaches
Compliance monitoring typically involves scanning transcripts for required phrases, prohibited language, and patterns such as phone numbers, financial account references, and personal data. For teams operating under GDPR, HIPAA, or PCI frameworks, the underlying infrastructure matters as much as the detection logic. Gladia's compliance hub documents the company's security certifications and compliance posture, including SOC 2 Type II and HIPAA compliance. On the Starter plan, customer data can be used for model training by default. On Growth and Enterprise plans, customer audio is never used for model retraining with no opt-out action required. PII redaction is available as an optional feature that requires explicit configuration.
"It's based in EU so it fits our GDPR compliance requirements... The team is very reactive and helpful... The product works great." - Robin L. on G2
Scaling agent development with AI
AI doesn't replace the coaching conversation. It eliminates the work that happens before it: identifying which calls to review and extracting the relevant moments.
Implementing 100% call coverage: a practical guide
Transcribing every contact center call
The transcript is the ceiling for everything downstream. An LLM scorecard can only score what the transcript contains, and named entity recognition can only extract names, numbers, and product references that were correctly captured. On noisy call center audio with multiple speakers, accented speech, and mid-conversation language switches, transcription errors compound at every downstream step.
Solaria-1, Gladia's proprietary speech model, delivers on average 29% lower WER than alternatives on conversational speech, benchmarked across 8 providers and 7 datasets. That accuracy advantage is particularly consequential for contact center audio, where noisy, multi-speaker conditions and mid-conversation language switches make transcription errors more likely and more costly downstream.
When to use live vs. post-call AI
For performance monitoring and QA, async batch transcription is typically the better architectural choice. The model processes the complete audio file with full context, which generally produces higher accuracy than streaming approaches. Diarization capabilities are optimized for async mode, where the full recording is available for speaker boundary resolution.
Real-time transcription at ~300ms latency is appropriate for live agent assist use cases where an agent needs an in-call prompt, but it trades accuracy for speed in ways that can create noise in QA scoring. For a QA scorecard evaluating compliance language, that accuracy difference matters.
Cost implications at 10x and 100x volume
The delta compounds when features are metered separately. A base transcription rate looks manageable until diarization, sentiment, NER, and translation are each added as line items. The table below models cost at three volume levels using Gladia's all-inclusive pricing against a typical fragmented stack where features may be added on top of a base transcription rate.
| Monthly volume |
Gladia Growth plan (from $0.20/hr, all-
inclusive) |
Typical fragmented stack (illustrative) |
| 100 hours |
Starting at $20 |
~$40-50 with add-ons |
| 1,000 hours |
Starting at $200 |
~$400-500 with add-ons |
| 10,000 hours |
Starting at $2,000 |
~$4,000-5,000 with add-ons |
See the full Gladia pricing page for feature availability by plan.
Three integration patterns for AI performance monitoring
AI performance monitoring within CCaaS
CCaaS-native AI tools (such as those in platforms like Talkdesk, Genesys, or Five9) are typically enabled through dashboard configuration and draw on the platform's proprietary transcription and scoring engines. They reduce initial deployment work by cutting the number of separate systems to configure.
The tradeoffs show up at scale and in multilingual environments. Many native tools have varying levels of support across different languages and regions, and non-English accuracy and code-switching detection can vary by platform. Pricing is often bundled into platform tiers, which can make it challenging to model the incremental cost of enabling QA features for an additional product line or language market.
Extend CCaaS with AI APIs
An add-on layer places a third-party transcription and intelligence API between your CCaaS recording layer and your QA scoring engine. Audio flows from the CCaaS platform to the API, structured outputs return to your QA dashboard, and the CCaaS platform continues to handle routing and agent interfaces.
Aircall uses exactly this architecture, routing calls through Gladia's API and using the transcription as the foundational layer for AI insights covering summaries, sentiment, and agent coaching. The result: transcription time cut by 95% (from 30 minutes to 1.5 minutes per call) at 1M+ calls processed per week.
Building your own AI monitoring stack
A custom build means owning the full pipeline: recording infrastructure, transcription API, parsing and storage logic, LLM scoring engine, and QA dashboard. It requires the most upfront engineering work but produces the lowest unit cost at scale, because there are no per-feature fees, and gives the most control over data handling and model choices. Multiple customers report sub-24-hour integration to production for the transcription API layer.
The audio-to-LLM pipeline documentation covers routing structured output directly to your scoring engine.The payload below is an illustrative example of the structured output shape. For the verified response schema, refer to the audio-to-LLM pipeline documentation. A single async API call returns speaker labels, word-level timestamps, sentiment scores, detected languages, and named entities in one JSON payload:
// Illustrative —- see docs.gladia.io for the verified response schema
{
"transcription": {
"full_transcript": "Hello, I need to cancel my subscription...",
"utterances": [
{
"speaker": "speaker_0",
"start": 0.5,
"end": 4.2,
"text": "Hello, I need to cancel my subscription.",
"language": "en",
"sentiment": "negative"
}
]
},
"entities": [...]
}
CCaaS-native vs. add-on layer vs. custom build
| Criteria |
CCaaS-native |
Add-on layer |
Custom build |
| Initial setup time |
Days to weeks |
Days to weeks |
Days to weeks (with API integration) |
| Multilingual accuracy |
Varies by platform |
Varies by provider |
Depends on vendor selection |
| Pricing predictability at scale |
Varies (bundled tiers) |
Varies (per-hour with possible add-ons) |
Per-hour all-inclusive models available |
| Data residency control |
Varies by platform |
Varies by provider |
Configurable with appropriate vendors |
| LLM model flexibility |
Platform-dependent |
Varies by integration |
Full control over model selection |
| Engineering overhead |
Low initial setup |
Medium |
Medium |
When to use native CCaaS AI
Native tools fit teams at early QA maturity who are monitoring English-language calls at low volume and don't need to customize scoring logic or swap transcription models. If your contact center runs on a single language and you need QA standing up in a week without engineering resources, native tools get you there faster.
Add-on layer: strategic fit criteria
The add-on pattern fits teams that have an existing CCaaS investment they want to extend and who need better multilingual accuracy, richer structured outputs, or independent pricing control. It's also the right pattern when you're building QA features into a product you sell to contact centers, rather than operating one yourself.
When unique needs demand custom AI
A custom build is the right fit for teams with non-standard data residency requirements, the need to swap transcription or scoring models independently, or unit economics that make a managed platform unviable at target volume. It also fits teams building QA infrastructure as a product they sell, where control over the full pipeline is a commercial requirement rather than a preference.
Scaling quality with automated AI scorecards
Defining scorecard criteria programmatically
A scorecard criterion applies a specific condition to the transcript: required disclosure spoken (yes/no), customer sentiment at call close (positive/negative/neutral), competitor name mentioned (entity extraction), or call resolved without escalation (boolean derived from summary). The table below maps data points to the QA insights they generate.
| Metric |
Definition |
QA insight |
| Sentiment score |
Positive/negative/neutral per speaker utterance |
Surfaces positive and negative customer experience patterns across calls |
| Script adherence |
Presence of required disclosure phrases |
Tracks consistency with required messaging |
| Named entity extraction |
Product names, competitors, account references |
Enables analysis of conversation content |
| Diarization error rate (DER) |
Accuracy of speaker attribution across audio |
Affects reliability of speaker-level analysis |
| Silence duration |
Total dead air time per call |
Surfaces dead air patterns linked to pacing issues or agent knowledge gaps |
| Overtalk ratio |
Percentage of time both parties speak simultaneously |
Indicates interruptions, poor call control, or conversational imbalance |
Sentiment score, script adherence, named entity extraction, and diarization error rate are available as structured outputs from Gladia's async API. Silence duration and over-talk percentage are QA concepts derivable from word-level timestamps and diarization output; they are not discrete fields returned by the API.
Eliminate bias in agent scoring
Human reviewers unconsciously favor agents they've had positive interactions with and calls that are personally easier to evaluate. An automated scorecard applies the same criteria to a call from a native English speaker and a call from a heavily accented speaker in a BPO environment without variation.
This is where multilingual accuracy becomes important. If the transcription layer produces significantly different accuracy across languages, the scorecard reliability may vary accordingly. For teams with language coverage spanning Southeast Asia (including BPO or Business Process Outsourcing operations), South Asia, or Latin America, consistent cross-language performance matters for reliable QA outcomes. The Attention x Gladia walkthrough video shows how this translates to CRM population and coaching scorecards in a revenue-critical call context.
Actionable AI insights for agent coaching
Empowering managers with AI feedback
The practical shift for QA managers is moving from "listen to these 20 calls and score them" to "review these flagged calls and decide if the coaching is warranted." Coaching quality improves because managers engage with specific patterns across many calls, not isolated impressions from a handful.
Key criteria for AI integration success
Benchmark WER for noisy audio
Run your own WER evaluation before committing to any transcription provider. Use real call recordings from your production environment, including background noise, overlapping speech, and the specific languages your agents handle. Gladia's async benchmark methodology is open and reproducible across 7 datasets and 74+ hours of audio covering conversational speech conditions.
"The speed and accuracy of the transcriptions is really solid, especially with challenging audio. I also like how easy the API is to setup, it works nicely without too much fiddling." - Adam B. on G2
Diverse accent accuracy testing
For CCaaS (Contact Center as a Service) platforms serving BPO operations in Southeast Asia, South Asia, or Latin America, accent robustness is an important selection criterion. Solaria-1 covers 100+ languages including many that have limited support from other API-level STT providers, including Tagalog, Bengali, Tamil, Urdu, and Punjabi.
Code-switching is a specific challenge in BPO environments where agents and customers shift between languages mid-call. Traditional ASR (Automatic Speech Recognition) systems face significant accuracy challenges when this happens.
Data residency and SOC 2 compliance
Enterprise legal reviews for audio infrastructure focus on three questions: where is data stored, who can access it, and does the vendor use audio to train models. EU and US regions are available, with data residency configurable per deployment. On the Starter plan, customer data can be used for model training by default. On Growth and Enterprise plans, customer audio is never used to retrain Solaria-1, with no opt-out action required.
Predictable AI scaling costs
Model your infrastructure cost at 100, 1,000, and 10,000 hours per month with all features enabled before signing any vendor contract. Features metered separately can produce an effective hourly rate significantly higher than the advertised base rate in a fragmented stack. On Gladia's plans, diarization, sentiment analysis, and NER are included in the per-hour base rate with no add-on fees.
Integration time: hours, not weeks
Achieving feature parity with cloud hyperscaler STT services for contact center deployments often requires integrating multiple services, building custom middleware, and managing separate billing configurations, each of which adds to total integration time. The async architecture guide walks through a complete pipeline from audio input to LLM-ready output.
Navigating common AI monitoring challenges
AI monitoring integration timeline
The integration timeline objection is usually about the QA dashboard, not the transcription layer. Multiple customers report sub-24-hour integration to production for the transcription API layer. The time-consuming work is defining scorecard criteria, building the routing logic for human review queues, and connecting outputs to existing CRM or workforce management systems. Set expectations accordingly: the speech infrastructure layer deploys fast, the workflow redesign takes longer.
Accuracy on noisy call center audio
Contact center audio often has characteristics that clean benchmark datasets don't fully reflect, such as background noise, VoIP compression artifacts, overlapping speech, and rapid topic transitions. The diarization explainer covers how speaker attribution errors propagate through downstream scoring. Testing on your own audio before going to production is the most reliable way to validate performance on conditions that match your environment.
AI's impact on human QA roles
The concern that AI replaces QA analysts misunderstands where their time goes. With manual sampling, an analyst spends most of their time on call selection and manual listening, with a fraction of their time on actual feedback and coaching. At 100% coverage, call selection is automated and manual listening drops to flagged calls only. The analyst's time shifts toward pattern analysis, training curriculum development, and high-confidence coaching conversations.
Modeling AI costs for 10x volume
The cost picture follows the same logic: scaling to 100% coverage changes how infrastructure costs are structured at volume, which is worth modeling before committing, but the prior section covers that in detail. The real question isn't whether you can afford to scale AI QA to 100% coverage: it's whether continuing to leave most conversations unmonitored is the right risk posture for your operation.
Start with 10 free hours to test Solaria-1 against your own call center recordings. Evaluate performance on the specific audio conditions your QA stack will process in production before committing your architecture to any transcription provider.
FAQs
What is the real cost difference between a custom API build and a fragmented stack at 10,000 hours?
At high monthly volumes, Gladia's all-inclusive pricing modelcan provide cost advantages compared to fragmented stacks charging diarization, sentiment, and NER as add-ons on top of a base transcription rate. The delta is most meaningful when you include the engineering overhead of managing separate billing relationships and debugging output mismatches between layers.
Does noisy call center audio affect downstream LLM scorecard accuracy?
Yes, directly. Every transcription error propagates as scorecard noise downstream. A transcript that drops a required compliance disclosure because of background noise produces a false compliance flag. Benchmarking your transcription provider against actual call recordings (not clean studio audio) before production is the only reliable pre-commit check.
Can Gladia handle mid-conversation language switches in a single call?
Yes. Solaria-1 detects language changes automatically across all 100+ supported languages without requiring a language parameter reset between speakers or segments, and this works in both async and real-time modes. Teams with BPO operations in bilingual markets can process calls with mid-conversation language switches without preprocessing or manual language tagging.
Does Gladia use customer audio to train its models?
On the Starter plan, customer data can be used for model training by default. On Growth and Enterprise plans, customer audio is never used to train Solaria-1, with no opt-out required.
How long does it take to integrate Gladia into an existing CCaaS pipeline?
Multiple customers report sub-24-hour integration to production. The transcription API layer connects via REST. The time-consuming part is building the downstream QA workflow: defining scorecard criteria, routing flagged calls, and connecting structured outputs to CRM or workforce management systems.
What is diarization error rate (DER) and why does it matter for QA?
Diarization error rate measures how often a transcription system misattributes a word to the wrong speaker, calculated as a percentage of total audio time incorrectly labeled. In a QA context, high DER means agent compliance language may be attributed to the customer and vice versa, producing invalid scorecard results. Gladia's async diarization achieves on average 3x lower DER versus alternatives on published benchmarks for conversational speech.
Key terms glossary
CCaaS (Contact Center as a Service): A cloud-based customer service platform that provides call routing, agent interfaces, recording, and analytics without on-premises hardware.
BPO (Business Process Outsourcing): Contracting third-party service providers to handle business operations, commonly used for customer support and call center operations in regions like Southeast Asia and Latin America.
ASR (Automatic Speech Recognition): Technology that converts spoken language into text, also known as speech-to-text (STT).
Word error rate (WER): The percentage of words in a transcript that differ from the reference transcription, calculated as substitutions plus deletions plus insertions divided by total reference words.
Diarization error rate (DER): The percentage of audio time incorrectly attributed to a speaker, including segments assigned to the wrong speaker, missed speakers, and false alarm speech regions.
Code-switching: Mid-conversation language changes where a speaker shifts from one language to another within the same utterance or across consecutive utterances.
Async (batch) transcription: Processing a complete audio file after recording completes, enabling bidirectional context for higher accuracy, full diarization via pyannoteAI Precision-2, and richer audio intelligence outputs.
Audio-to-LLM pipeline: The process of converting raw audio into structured transcript data (with diarization, sentiment, and entities) and routing that data to a large language model for downstream scoring or summarization.
Text-based sentiment analysis: Classifying transcript text as positive, negative, or neutral using NLP models operating on lexical content. Distinct from acoustic emotion detection, which analyzes vocal characteristics such as pitch, tone, and energy in the raw audio waveform.
PII redaction: The removal or masking of personally identifiable information (names, phone numbers, financial account references) from transcript output. This feature requires explicit configuration and is not enabled by default.
