Most product teams building contact center AI obsess over which large language model (LLM) to use for summaries and coaching, while a 15% word error rate in the audio capture quietly makes those LLM outputs unreliable. The bottleneck isn't the model at the top of the stack. It's the transcript at the bottom.
Contact centers deploy AI to automate QA, summarize calls, route customers, and assist agents in real time. For product teams building these platforms, the ROI of every downstream feature is capped by the WER of the initial transcript. Per Talkdesk's analysis, one minute saved by AI compounds to $17,424 in total savings over a year, based on their stated agent salary burden and call volume assumptions. That figure assumes accurate, complete transcripts feeding the AI layer. Here is how AI improves efficiency across eight specific workflows, the benchmarks to target, and why the audio infrastructure layer determines whether any of it works.
Core drivers of AI efficiency in contact centers
The shift from manual to AI-assisted operations isn't a single change. It's a stack of compounding improvements, each dependent on the layer below it. The table below captures the practical difference between traditional and AI-driven processes, and what makes each gain possible.
| Process |
Manual approach |
AI-driven approach |
Accuracy dependency |
| QA coverage |
1–5% of calls reviewed |
AI platforms can score all calls |
QA scoring reliability is capped by transcript WER |
| Post-call documentation |
Manual notes per call |
Auto-generated in seconds |
Summary quality ceiling-bounded by transcript accuracy |
| Routing accuracy |
IVR menu selection |
Intent detection from spoken input |
STT errors before natural language understanding (NLU) cause misroutes |
| Knowledge retrieval |
Agent searches manually |
Surfaced mid-call in real time |
Assist layer reads the live transcript |
| Demand forecasting |
Historical spreadsheets |
ML on structured call data |
Forecast precision requires accurate call intent data |
| Sentiment escalation |
Supervisor judgment |
Threshold-triggered, every call |
Sentiment classification derived from transcribed text |
Enabling real-time agent assist
The catch with real-time agent assist is immediate: this layer is only as useful as the words it reads. A transcript that renders "account cancellation" as "account cancelation request form" routes knowledge retrieval in the wrong direction, and the agent still has to search manually. When the transcript is accurate, agent assist surfaces knowledge base articles, customer relationship management (CRM) records, and response suggestions during live calls rather than after, eliminating the dead time between understanding a customer query and locating the answer. Contact center ROI analysis documents that after-call work reduction from AI assist tools can free significant agent capacity per shift by removing administrative overhead.
Automated call routing and intent detection
Smart routing reduces handle time and improves FCR
AI-powered routing analyzes the customer's spoken intent at the start of a call rather than waiting for them to navigate an interactive voice response (IVR) menu. Natural language understanding extracts the purpose of the call from the transcribed speech, matches it to the right queue or agent, and routes within seconds. The practical result is fewer transfers, shorter average handle time, and less re-verification after handoffs.
First contact resolution (FCR) is a key metric in contact center operations, influencing both operational efficiency and customer satisfaction. When AI routes a call to the right agent the first time, FCR rates improve, reducing costly repeat contacts and transfers.
Benchmarking call routing accuracy
Intent detection accuracy for contact center use cases depends heavily on the quality of the underlying transcript, per speech pattern research on call routing. Accuracy drops in multilingual environments when the STT layer misidentifies the language or distorts accented speech before the NLU model reads it. Fixing routing accuracy means fixing transcription accuracy first.
AI-driven self-service for customer support
Setting AI containment targets
For well-implemented AI self-service, most customers resolve their issue without speaking to an agent. The gap between lower and higher containment results typically traces back to multilingual transcription accuracy in the self-service layer.
Reduced agent workload via AI
When AI handles routine FAQs, agents handle complex cases. Zendesk's research on AI call centers documents how AI-powered systems can deflect routine inquiries to faster resolution paths. Fewer routine calls per agent means more time per complex interaction, which lifts quality scores on the calls that matter most.
Improving multilingual AI accuracy
Contact centers serving Southeast Asia, South Asia, or Latin America face a specific failure mode: the AI model works in English and breaks on Tagalog, Bengali, or Marathi. Languages that represent high call volumes for business process outsourcing (BPO) operations often have the weakest support from mainstream STT providers.
Gladia's Solaria-1 model covers 100+ languages including 42 that no other API-level STT provider supports. For contact center platformsserving these populations, language coverage depth isn't a feature comparison. It's the difference between a product that works and one that doesn't. Solaria-1 also handles true mid-conversation code-switching, detecting language changes within a single call without requiring a language declaration at the start of a session.
Empowering agents with live AI guidance
Reduce agent search delays
Real-time assist tools surface relevant CRM records and suggested responses mid-call, eliminating the tab-switching and manual search that adds overhead to every interaction. Removing the dead time between customer query and answer retrieval addresses one of the highest cost drivers in high-volume contact centers. This layer works correctly only when the underlying transcript delivers the right words to the retrieval system. A correctly transcribed customer query routes to the right knowledge article. A garbled one routes to nothing useful.
Agent assist latency benchmarks
Many contact center AI workflows (post-call summaries, QA scoring, CRM enrichment) rely on async transcription for higher accuracy at lower cost. For live-assist use cases, the latency budget between speech and surfaced suggestion needs to stay inside a conversational window. Gladia supports real-time transcription with approximately 300ms final transcript latency as a secondary capability for these workflows. Gladia's documentation covers deployment patterns for live-assist integrations in detail.
Automated post-call summaries and documentation
Reduce ACW with AI summaries
After call work (ACW) is one of the highest-volume cost categories in contact center operations. Manual post-call documentation consumes agent time on disposition notes, action items, and CRM updates before the next call begins. AI-generated summaries cut this to seconds by producing structured outputs directly from the transcript.
The Aircall case study is the most concrete benchmark available: Aircall cut transcription time by 95%, reducing per-call processing from 30 minutes to 1.5 minutes, and now processes over 1 million calls per week through Gladia. The AI summary layer powers search, coaching, sentiment analysis, and CRM webhook updates from a single integration.
Diarization accuracy benchmarks
Summaries that merge two speakers into one transcript are useless for coaching and compliance. A transcript that attributes the agent's compliance statement to the customer, or vice versa, fails the QA rubric before the LLM even reads it. Diarization (the process of attributing speech segments to specific speakers) is what makes summaries actionable.
Gladia's async diarization powered by pyannoteAI Precision-2 delivers on average 3x lower DER than alternatives, based on the open, reproducible methodology published in Gladia's async benchmark.
Automated CRM record enrichment
The Audio-to-LLM pipeline takes the diarized, transcribed call and runs named entity recognition, summarization, and sentiment analysis. The output is structured data designed to integrate with CRM systems and downstream workflows. Named entities extracted from calls can support automated CRM field updates when integrated into your existing systems.
Automated QA with 100% call coverage
100% call coverage replaces manual sampling
Manual QA teams, even well-resourced ones, review 1–5% of total interactions, meaning most calls go unreviewed and compliance violations, script deviations, and coaching opportunities disappear with them. Automated QA platforms can score all calls by running the transcript against predefined rubrics, compliance scripts, and policy checklists, shifting QA teams from primary review to validation and calibration.
Selectra's deployment shows what this shift looks like in production. After integrating Gladia, Selectra's QA team validates AI findings rather than doing the primary review work themselves. The LLM assesses compliance and extracts insights from the transcript. Human reviewers confirm or override.
WER: key to reliable QA scores
Automated QA's reliability is a direct function of transcript accuracy. If the transcript renders "I've reviewed your account and confirmed your cancellation" as "I've reviewed your count and confirmed your cancellations," the compliance check against the required phrase fails, the QA score drops, and a compliant agent gets flagged as non-compliant.
This is the failure mode that kills confidence in automated QA. High WER means transcription errors corrupt compliance phrase matching. QA scoring on transcripts at high error rates produces results that require manual verification on almost every flagged call, reconstructing the manual review bottleneck you were trying to eliminate.
Forecast contact center demand with AI
Optimized staffing with AI forecasts
Workforce management systems use machine learning on historical call data to predict future demand and schedule agents to match it, rather than maintaining excess capacity for worst-case volumes, as documented in MaestroQA's analysis of call center cost-per-call. The input to any accurate forecast is a structured dataset of historical call intent patterns, which requires accurate transcription of the underlying calls. Accurate volume forecasting allows operations teams to maintain service level agreements without proportional headcount increases as call volume grows.
Proactive churn prevention with sentiment AI
AI escalation accuracy benchmarks
Text-based sentiment analysis classifies emotional tone from the words in the transcript, not from vocal acoustic characteristics. This distinction matters: sentiment analysis on a transcript tells you what the customer said and how they phrased it. It is not acoustic emotion detection, which analyzes pitch and vocal patterns from the raw audio waveform. Gladia provides text-based sentiment inference as part of its audio intelligence suite.
Sentiment classification accuracy is bounded by transcript accuracy. A frustrated customer saying "this is completely unacceptable" produces a clear negative sentiment signal when transcribed correctly. When transcribed as "this is completely unexceptable," the signal weakens and threshold triggers may not fire.
Reducing customer churn risk
AI sentiment monitoring on every call identifies frustration signals that would never surface in limited manual review samples. A customer who mentions a competitor three times in one call and expresses repeated dissatisfaction is a churn risk. Automated flags on 100% of calls give retention teams an actionable queue that manual review can't generate at scale.
Inaccurate transcripts: the hidden cost to ROI
Transcription errors degrade AI value
Every efficiency gain covered in this article sits downstream of the transcript. The LLM generating your post-call summary reads the transcript. The QA scoring model reads the transcript. The CRM enrichment pipeline reads the transcript. A wrong name corrupts a CRM entry silently. A missed compliance phrase fails a QA score incorrectly. A misidentified speaker ruins a coaching scorecard. These failures don't announce themselves loudly. They accumulate until your users stop trusting the AI outputs and your engineering team starts spending time on fixes that trace back to a bad transcript.
Uncovering hidden accuracy costs
Add-on pricing structures from some STT providers create a second cost problem on top of accuracy issues. Features like diarization, redaction, and multichannel audio are billed separately by several vendors, and rates can increase if you opt out of the model improvement program, as documented in Gladia's Deepgram pricing breakdown. The hidden cost of inaccurate transcription isn't the transcript itself. It's the downstream human review required to correct AI outputs built on bad data, along with the engineering time spent diagnosing failures that originate one layer below where you're looking.
Production WER: the proof
Gladia's async benchmark evaluates Solaria-1 against eight providers using an open, reproducible methodology. On conversational speech, Solaria-1 achieves on average 29% lower WER than alternatives and on average 3x lower DER (diarization error rate). For the CCaaS use case specifically, where accented speech, overlapping speakers, and code-switching are the norm rather than the exception, these differences compound across every call processed.
Building contact center AI on production-grade infrastructure
Meeting production WER targets
Evaluating STT providers on clean, studio-quality audio produces results that don't transfer to production. Contact center audio is noisy, multi-speaker, and multilingual. Run vendor evaluations on your actual call recordings, including calls with accented speakers, background noise, and mid-conversation language switches. The Gladia benchmark methodology covers seven distinct datasets including conversational speech specifically because clean audio benchmarks don't predict production performance.
Low WER on your specific audio profile is essential for accurate downstream AI outputs. High error rates translate directly to the volume of AI-generated outputs requiring human correction, which reconstructs manual overhead in a different form.
Non-English accuracy benchmarks
Evaluating STT providers for non-English accuracy in contact center environments reveals a common failure mode: strong English performance doesn't predict reliability on high-volume BPO languages like Tagalog, Bengali, Punjabi, Tamil, Urdu, or Marathi. Coverage gaps and WER divergence across language families mean that a provider benchmarked on English conversational speech may fail silently on the languages your call center actually processes at scale.
Solaria-1 supports 100+ languages including 42 that no other API-level STT provider covers. For contact centers processing calls in these languages, this isn't a feature comparison. It's whether the product works for that user population at all. The code-switching documentation covers how Gladia handles mid-conversation language changes across all supported languages in both real-time and async modes.
How does pricing scale with volume?
On Starter and Growth plans, audio intelligence features (diarization, translation, NER, sentiment analysis, summarization, code-switching) are included in the base rate with no separate add-on fees. Enterprise pricing is debundled; features are scoped per contract.
| Plan |
Async |
Real-time |
Notes |
| Starter |
$0.61/hr |
$0.75/hr |
Pay-as-you-go; 10 hours free monthly. Customer data can be used for model training by default. |
| Growth |
As low as $0.20/hr |
As low as $0.25/hr |
Upfront commitment required. Customer data never used for model training; no opt-out required. |
| Enterprise |
Custom |
Custom |
Annual plan; debundled features, fine-tuning, zero data retention. Customer data never used for model training. |
Pricing scales with volume across Starter, Growth, and Enterprise plans. On Growth and Enterprise plans, customer audio is never used to retrain models, and no opt-out action is required. On the Starter plan, customer data can be used for model training by default. Enterprise offers custom pricing with debundled features, fine-tuning, and zero data retention. Full detail is on Gladia's per-hour pricing page. Gladia is SOC 2 Type II, ISO 27001, HIPAA, GDPR, and PCI compliant. Additional compliance details are documented at the compliance hub.
The architecture difference matters for product teams: Gladia covers the full audio pipeline (record, transcribe, enrich), which can consolidate multiple vendor integrations in a typical contact center AI stack. One bill, one integration point, one accuracy standard to evaluate. Multiple customers report sub-24-hour integration to production using the Python and JavaScript SDKs.
Pre-deployment infrastructure checklist
Before deploying AI across your contact center workflows, verify the STT layer meets these production requirements:
- Benchmark STT accuracy on your actual call recordings, not clean demo audio
- Test WER on your highest-volume non-English languages separately
- Verify diarization accuracy (DER) on calls with three or more speakers
- Confirm code-switching handling if your agents or customers switch languages mid-call
- Model total cost at 1,000 and 10,000 hours with all features enabled (NER, diarization, sentiment) to verify that per-hour pricing holds at 10x and 100x current volume without add-on fees appearing
- Confirm the vendor's data training policy by pricing tier in writing before signing
- Check compliance certifications against your requirements (SOC 2 Type II, HIPAA, GDPR)
- Measure ACW reduction after deploying AI summaries with a 30-day pilot
- Set a baseline QA coverage percentage before AI deployment to measure improvement
- Validate that routing intent detection accuracy holds on accented and multilingual audio
Get started with Gladia and test Solaria-1 on your own call recordings to see how it handles accented speech, code-switching, and multi-speaker audio in your actual production conditions. Multiple customers report sub-24-hour integration to production.
FAQs
What is a realistic AI containment rate for a contact center self-service deployment?
For well-implemented AI self-service, most customers resolve their inquiry without reaching a human agent. The gap between higher and lower containment results typically traces back to multilingual transcription accuracy in the self-service layer.
How much can AI reduce post-call documentation time?
AI-generated post-call summaries cut manual documentation from minutes to seconds per call. Aircall documented a 95% reduction in transcription and processing time, from 30 minutes to 1.5 minutes per call, after integrating Gladia's STT API across more than 1 million calls per week.
How does word error rate (WER) affect automated QA reliability?
High WER causes transcription errors that make compliant agents appear non-compliant and require QA teams to manually verify AI findings rather than validate them. Targeting low WER on your actual call audio removes this review bottleneck.
What is Gladia's pricing for contact center audio at scale?
Gladia charges per hour of audio duration, with diarization, NER, sentiment analysis, and summarization included in the base rate on Starter and Growth plans. Growth and Enterprise plans never use customer audio for model retraining, with no opt-out required.
Key terms glossary
Word error rate (WER): The percentage of words in a transcript that differ from the actual spoken words, calculated as substitutions plus insertions plus deletions divided by total words. WER is the primary metric for evaluating STT accuracy and directly determines the reliability of every downstream AI feature built on the transcript.
Diarization error rate (DER): A metric measuring how often a speech-to-text system misattributes speech to the wrong speaker, misses speech segments, or hallucinates speech. DER determines whether post-call summaries, QA scores, and coaching reports correctly attribute which agent or customer said what.
Code-switching: The phenomenon where a speaker alternates between two or more languages within a single conversation or even a single sentence. Code-switching is common in multilingual contact centers and breaks most STT APIs that require a fixed language declaration at the start of a session, causing silent transcription failures on high-value calls.
After call work (ACW): The time an agent spends on documentation, CRM updates, and disposition codes after a customer call ends. ACW represents one of the highest-volume efficiency targets for AI automation in contact center operations, and AI-generated summaries eliminate most of the manual steps in that workflow.
