A single misheard word in a contact center call doesn't just ruin a transcript. It silently corrupts your CRM entry, invalidates your automated QA score, and misleads your coaching pipeline, and no alert fires. By the time a supervisor notices a scorecard that doesn't match agent behavior, the error has already propagated through dozens of downstream records. The operations leaders who close this gap through call center automation don't do it by hiring more QA analysts; they do it by fixing the data quality of the audio layer underneath every automation they deploy.
Modern automation has evolved well beyond basic IVR trees. Today's AI systems can autonomously execute multi-step workflows and routing decisions rather than following rigid, pre-programmed scripts, coordinating interactions across channels, systems, and agents to deliver a consistent service experience at scale. These capabilities depend entirely on one thing: an accurate, structured record of what was actually said on every call.
How call center automation improves contact center ops
How automation covers the call journey
The call lifecycle runs from the moment a customer dials in to the moment their issue is logged, closed, and analyzed. Automation can touch every stage of that journey, and the operational value compounds when each stage produces reliable structured data. The layers map to the KPIs that matter most:
- Pre-call routing and IVR: Natural language understanding can classify intent and route calls without a menu tree, potentially reducing containment rate failures and abandonment rate.
- Self-service and voicebots: Automated resolution for tier-1 inquiries can keep Average Handle Time (AHT) on live agents focused on complex interactions and improve cost-per-contact.
- Real-time agent assist: Live transcription can feed next-best-action prompts during the call, potentially reducing AHT and improving First Call Resolution (FCR) by surfacing knowledge base entries mid-conversation.
- Post-call QA and summarization: Async transcription of the full recording enables automated scoring, disposition tagging, and Customer Relationship Management (CRM) logging, without a human listening to every call. Understanding which automation use case to prioritize for your operation prevents the classic failure mode: automating the wrong stage first and then wondering why QA coverage hasn't improved.
Turning voice data into actionable ops
Raw audio remains operationally inert until it becomes structured text. The transcript is what your CRM ingests, your QA platform scores, your coaching tool analyzes, and your compliance team audits. Aircall cut transcription time by 95% and now processes 1M+ calls per week through us, outcomes that depend on the transcription layer being accurate at scale. When the transcription layer degrades, automation gains shrink, because every downstream system runs on corrupted data.
The contact center architecture modernization guide frames the compliance risk directly: transcription errors in consent statements or compliance disclosures create legal and regulatory risks at the STT layer, propagating through every downstream compliance system and surfacing only during audits.
Where to deploy AI in your support workflow
AI-powered routing and self-service
AI-powered routing can classify caller intent from natural language rather than Dual-Tone Multi-Frequency (DTMF) keypresses, potentially producing fewer mis-routed calls, lower abandonment rate during queue transfers, and tighter Service Level Agreement (SLA) adherence. Understanding how AI determines caller intent is the architectural starting point for any IVR modernization project, and a structured AI call flow design covers intent classification, fallback handling, and escalation triggers, common points where automated routing can break under real-world call volumes.
Voicebots can handle tier-1 inquiries, account lookups, status checks, and appointment scheduling without agent involvement, potentially reducing cost-per-contact and easing staffing pressure during peak hours. High-frequency, low-complexity tasks like password resets, balance inquiries, and order status checks are typically strong containment candidates, while complex billing disputes, escalated complaints, and scenarios requiring judgment or empathy generally are not.
Live agent copilots for complex calls
Real-time transcription can feed agent copilots with live context: relevant policy snippets, suggested responses, compliance disclosures, and CRM data surfaced mid-call. The potential operational effect is lower AHT on complex calls as agents spend less time searching knowledge bases while the customer waits.
Scaling QA coverage with AI
Most contact center QA teams manually review only a small fraction of calls, leaving the vast majority of interactions without quality review. Automated QA can run scoring logic across 100% of transcripts, applying your scorecard criteria consistently at any volume. The prerequisite: a transcript accurate enough that the scoring logic finds the compliance keyword, the correct disclosure, and the sentiment signal you need.
Automating call summaries and tagging
Post-call wrap-up can consume several minutes per call for manual disposition and note entry. Automated summarization and entity extraction can significantly reduce that time and improve the consistency of agent-authored notes. Our audio intelligence suite produces structured output from each call, containing text-based sentiment scores, named entity recognition results, key topics, and summaries.
Scaling QA coverage without increasing headcount
Reducing AHT while maintaining FCR
Automated post-call summaries can reduce wrap-up time, and real-time assist during the call may reduce AHT by surfacing relevant information faster. Our CCaaS use case page outlines how contact center platforms operationalize this cost shift.
The operational tension appears when automation reduces AHT by shortening wrap-up time, but FCR degrades because agents close tickets faster without actually resolving the underlying issue. Automated post-call summaries can compress wrap-up from several minutes to seconds, lowering AHT across the board. Real-time assist during the call may surface relevant knowledge base entries or suggested responses faster than manual search, also reducing handle time. But if the transcription layer feeding those real-time prompts misreads a product name, account number, or technical term, the agent follows incorrect guidance, the customer's issue goes unresolved, and the call returns to the queue as a repeat contact. The transcript accuracy determines whether the AHT gain compounds with higher FCR or gets offset by repeat calls that never should have happened.
Automating 100% of interaction reviews
Moving from manual spot-sampling to 100% automated review changes what your QA team does rather than eliminating it. Instead of listening to calls, your QA team validates scoring logic, investigates flagged anomalies, and builds better rubrics from aggregate data.
Standardizing QA across Business Process Outsourcing (BPO) sites
Offshore and nearshore BPO sites introduce accent, dialect, and code-switching complexity that breaks QA frameworks built for American English. When the transcription layer struggles with accented speech, it can create data quality challenges that affect QA consistency across regions.
The operational risk appears clearly in research on factors affecting STT accuracy, which identifies speaker accents and regional dialects as primary degradation factors for systems not trained on multilingual audio. Our model Solaria-1 supports for 100+ supported languages, including Tagalog, Punjabi, Tamil, and Bengali, addresses this challenge for BPO operations running across Southeast Asia, South Asia, and Latin America.
Reducing turnover via agent assist
Contact center attrition carries a compounding structural cost: each departure triggers a recruiting cycle, an onboarding period, and a ramp-up phase before a replacement reaches full productivity, a pattern that high-volume sites repeat continuously across dozens of agents at any given time. Reducing administrative burden is one of the most consistent attrition levers available: agents who spend less time on manual wrap-up, note entry, and repetitive knowledge base searches may report lower burnout, and lower burnout can reduce early exits. Automated call summaries, real-time knowledge base surfacing, and consistent coaching feedback derived from accurate transcripts all can reduce the administrative friction that accelerates departure decisions.
Defining the boundary: AI tasks vs agent empathy
Driving deflection via voicebots
Voicebot containment works for high-frequency, low-complexity inquiry types: account lookups, FAQ responses, status updates, appointment confirmation, and basic troubleshooting flows. The right containment target depends on your specific call mix. Callers with complex issues who get trapped in automation that can't help them will reflect that frustration in CSAT, so containment strategy needs to start from actual call distribution data, not industry benchmarks.
Automated QA for consistent audits
Automated QA applies the same scoring criteria to every call, removing inter-rater variability that makes manual QA scores unreliable as a compliance audit trail. For regulated industries, this consistency matters because it produces a defensible record of every interaction against your compliance rubric. The AI transcription legality guide confirms what your transcription vendor's data handling must meet before deployment.
When to escalate to live agents
Clear escalation triggers prevent voicebots and automated workflows from frustrating customers. Effective escalation logic routes to a live agent when:
- Sentiment degrades: Multiple consecutive turns with negative text-based sentiment signal the automated flow is failing the customer.
- Intent is ambiguous: When the voicebot cannot classify intent with sufficient confidence after two attempts, human judgment reduces mis-routing risk.
- Complexity threshold is exceeded: Billing disputes, regulatory complaints, or multi-factor account changes exceed voicebot capability by design.
- Customer requests an agent: No containment rate target justifies blocking a direct escalation request.
Balancing automated QA and human empathy
The strategic question is augmentation versus replacement, and the operational evidence favors augmentation. AI handles volume, consistency, and structured data extraction. Human agents handle judgment, empathy, and complex problem resolution. The Scaling Conversations With 15x ROI webinar explores how production voice AI deployments maintain CSAT by keeping humans in the loop for escalation-worthy interactions.
Hybrid workforce model comparison
| Perspective |
Core philosophy |
Key vendors |
Operational impact |
| Augmentation |
AI reduces agent burden and surfaces information faster, humans retain decision authority |
Dialpad, Salesforce, Convoso |
Lower AHT, higher FCR, reduced burnout, coaching driven by 100% interaction data |
| Replacement |
AI agents handle the full call lifecycle autonomously, human agents handle exceptions only |
Synthflow |
Lower headcount costs, risk of CSAT degradation on complex calls if escalation logic fails |
How AI orchestrates the modern call lifecycle
Turning raw audio into actionable data
A single API call returns the full structured record your QA and CRM systems need: word-level timestamps, speaker labels, named entities, text-based sentiment, and a summary. Under the hood, our async-first pipeline uses Solaria-1 with pyannoteAI's Precision-2diarization model to produce that output. The audio-to-LLM pipeline documentation covers how to route that structured output to any LLM for downstream analysis.
Text-based sentiment inference runs NLP models over the transcript to classify each speaker turn as positive, negative, or neutral. Named entity recognition extracts account numbers, product names, and agent names directly from the transcript, reducing manual tagging workload and improving CRM entry accuracy.
Bridging call data to CRM and WFM
Structured transcripts and summaries push to CRMs like Salesforce and WFM systems through standard REST integrations. Our integration recipes guide covers integration paths for connecting call data to your workflow stack. Deciding what call data CRM needs before building the integration prevents the common mistake of logging every field and then finding the data unusable because it lacks consistent structure.
Operationalizing AI for measurable cost impact
Measuring impact on core CX metrics
Define your pre-deployment baseline across FCR, AHT, CSAT, and cost-per-contact before launching any automation, because without a clean baseline, attributing post-deployment metric movement to a specific intervention becomes guesswork. Track QA coverage rate as a leading indicator: moving from manual spot-sampling to 50% automated coverage in the first 30 days signals the transcription and scoring pipeline is functioning correctly before committing to 100% coverage. AHT impact from automated wrap-up typically appears within the first billing cycle because it removes minutes per call at scale.
Budgeting for unforeseen AI expenses
Base platform rates look competitive in an RFP until diarization, sentiment analysis, translation, and entity extraction each appear as separate line items. At scale, add-on fees for these features materially inflate the effective per-hour rate compared to the headline price. Our per-hour pricing includes diarization, translation, NER, sentiment analysis, summarization, and code-switching at the base rate on Starter and Growth plans, with no add-on fees on either tier.
"Accurate Fast and Developer Friendly Transcription API for Multilingual Audio... The pricing model could be clearer for large volume enterprise use." - Faes W. on G2
Avoiding pilot failure in production
Pilots fail when evaluation audio doesn't match production audio. Studio-quality recordings with native English speakers produce benchmark WER numbers that fail to translate to real call center audio: overlapping speech, background noise, accented speakers, regional dialects, and code-switching all degrade accuracy on models not specifically designed for production conditions. Our compareSTT tool lets you run your own blind comparison of Solaria-1 against competing providers on real audio (including accented and multilingual calls from your BPO sites), so you can evaluate performance on your actual production conditions before committing. For European contact-center audio in EN, FR, DE, ES, IT, Solaria-3 is our most accurate model, ranked #1 against AssemblyAI, ElevenLabs, Deepgram, Mistral, and Speechmatics on real business audio benchmarks, with 6.4% WER on Earnings22, the only model under 7%.
Technical benchmarks for call center AI
| Metric |
Target benchmark |
Operational impact |
Gladia performance |
| Word Error Rate (WER) |
As low as possible on conversational audio |
Transcription errors propagate into QA scores, CRM entries, and coaching data |
On average 29% lower WER vs alternatives on conversational speech; for European and contact-center audio in EN, FR, DE, ES, IT |
| Diarization Error Rate (DER) |
Minimized on multi-speaker calls |
Incorrect speaker attribution misattributes compliance disclosures to the wrong party |
Powered by pyannoteAI Precision-2 for superior speaker attribution |
| Latency budget (async) |
Fast processing per hour of audio |
Delays post-call QA and CRM logging if processing is slow |
Optimized async processing pipeline |
| Language support |
Covers all BPO operating languages |
Missing language support creates a two-tier QA system |
100+ languages with code-switching |
Why scaling contact center AI often falters
Handling accented speech in automation
Standard speech-to-text engines trained primarily on American English degrade measurably on accented speech, and that degradation clusters in exactly the BPO geographies most commonly used: Southeast Asia, South Asia, and Latin America. Solaria-1 addresses this directly. Automatic language detection identifies the speaker's language without requiring a language parameter upfront, and code-switching detection tracks context across the full conversation when speakers alternate languages mid-call.
Overcoming data silos in legacy systems
Legacy telephony infrastructure wasn't designed to output structured call data to modern analytics and QA platforms. Most migrations run longer than projected because compatibility testing between the recording layer, the transcription API, and the CRM or WFM destination surfaces integration gaps not visible in the RFP.
Meeting compliance for contact center AI
Regulated industries require documented data handling, clear audit trails, and certifications that survive a compliance review. Vendors that bury data retention policies or model-training defaults in terms of service create liability that surfaces during audits, not during procurement. Our compliance hub confirms certifications explicitly rather than requiring legal review to find them.
Compliance and governance checklist for contact center AI procurement:
- SOC 2 Type II: Verified annual audit of security controls covering availability, confidentiality, and processing integrity.
- ISO 27001: Internationally recognized information security management standard.
- HIPAA: Required for operations processing protected health information in healthcare contact center workflows.
- GDPR: Required for any operation handling EU customer data, regardless of where processing occurs.
- Model training policy by tier: Confirm whether customer audio is used to retrain models on each pricing tier and what opt-out mechanisms exist.
- Data residency: Confirm which geographic regions data is processed and stored in.
Operational stability during growth
Infrastructure that runs reliably at 10,000 calls per week often breaks under a different failure mode at 1 million calls per week: capacity planning, burst handling, and concurrent session limits become operational constraints rather than theoretical ones.
Addressing risks in call center AI deployment
Setting realistic targets: Deflection targets need to reflect your actual call mix. Callers with complex issues pushed through automation that can't resolve them will register that frustration in CSAT scores. Track CSAT at the segment level after deployment: self-contained (fully automated) calls separately from escalated (human-handled) calls. If CSAT on fully automated calls degrades while escalated call CSAT holds, the escalation trigger logic needs adjustment, not the automation itself.
Modeling ROI and migration decisions: ROI timelines for contact center AI deployments vary based on deployment scope and whether legacy platform migrations are involved. Our voice AI unit economics webinar covers cost modeling methodology for teams operating at significant call volumes. You don't need to migrate your Customer Relationship Management (CRM) or CCaaS platform to upgrade your audio infrastructure: contact center platforms can accept a third-party STT API for post-call processing, which means you may be able to replace a weak transcription layer without a full platform migration. If your current transcription accuracy is degrading QA scoring or blocking multilingual BPO expansion, upgrading the transcription API is often a faster, lower-risk path.
Get started with us and test Solaria-1 against your own production audio, including accented and multilingual calls from your BPO sites. Teams can typically move to production quickly, with engineering support available through the integration.
FAQs
What is the typical cost reduction from call center automation?
Production deployments show what's achievable when the full pipeline is accurate: Aircall cut transcription time by 95% and now processes 1M+ calls per week. QA and coaching automation running on degraded transcripts reduces the effective savings, because every downstream system inherits the errors from the transcription layer.
Does Gladia use customer data to train its models?
On Growth and Enterprise plans, your audio is never used to train our models. On the Starter plan, data can be used for model training by default. See our pricing page for tier details.
What certifications does Gladia hold for data security?
We hold SOC 2 Type II, ISO 27001, HIPAA, GDPR, and PCI certifications. Full details are in our compliance hub.
Is real-time speaker diarization supported?
Speaker diarization, powered by pyannoteAI's Precision-2 model, is available in async (batch) workflows only. For real-time transcription use cases, speaker attribution can be handled in post-processing for higher accuracy.
What does Gladia's all-inclusive pricing actually include?
The Starter and Growth plans include diarization, translation, sentiment analysis, NER, summarization, custom vocabulary, and code-switching with no add-on fees. See pricing details for current rates.
How does accented speech affect automated QA accuracy?
Accented speech raises WER on models not trained for multilingual robustness, and those transcription errors propagate into QA scores, coaching data, and CRM entries. Solaria-1 is optimized for conversational speech across 100+ supported languages, including audio from accented speakers.
Key terms glossary
Word Error Rate (WER): The standard metric for speech-to-text accuracy, calculated by comparing the automated transcript against a human-verified reference. Lower WER means fewer errors feeding downstream systems like QA scoring and CRM logging.
Diarization Error Rate (DER): The metric that measures how accurately an AI model identifies which speaker said what and when. Lower DER means more reliable speaker attribution in multi-speaker calls.
Code-switching: Alternating between two or more languages or dialects within a single conversation, common in BPO environments serving bilingual caller populations. Solaria-1 handles mid-conversation code-switching natively without requiring a language to be specified upfront.
