Most contact center operations leads select transcription software based on clean-audio lab benchmarks, only to watch automated QA systems fail in production when confronted with accented BPO speech, noisy phone lines, and mid-call language switches. The underlying problem isn't the QA platform or the CRM connector, it's the transcription layer breaking under the speech conditions your vendor never tested on.
Transcription isn't just a text output. It's the data foundation your entire downstream AI stack runs on. When the speech-to-text layer produces a wrong name, misses a compliance disclosure, or garbles a phone number, that error doesn't stop at the transcript: it corrupts the CRM entry, skews the sentiment score, and renders the QA scorecard unreliable. To scale QA coverage without proportionally growing headcount, you must treat transcription as critical data infrastructure and evaluate vendors against four pillars: real-world multilingual accuracy, predictable per-hour unit economics, strict data residency, and direct integration pathways.
How transcription accuracy directly improves call center metrics
We see the CCaaS use case for transcription play out the same way across contact centers: record every call, transcribe it accurately, extract structured intelligence, and push results downstream. The gap between "we have transcription" and "our QA data is reliable" is where most operations teams lose productivity.
Scaling QA from sampling to 100% coverage
In most contact center operations, QA teams sample a fraction of interactions manually, which makes it statistically difficult to catch individual compliance violations or identify early agent performance indicators before they compound. Automated transcription with high enough accuracy to trust QA scoring changes that equation directly, because when every call is transcribed and scored automatically, QA teams shift from manually reviewing sampled calls to validating AI findings against flagged edge cases.
Understanding which automation use case to prioritize matters before selecting a vendor, because the accuracy threshold you need depends heavily on whether you're scoring compliance disclosures, coaching scorecards, or CRM population.
Securing call data and audit trails
Recording and transcribing customer calls in a regulated environment means satisfying overlapping legal frameworks, but the real operational value is creating an immutable audit trail: timestamped, speaker-attributed transcripts that document exactly what was said by whom and when. GDPR grants individuals rights regarding their voice recordings. HIPAA requires that protected health information in any form, including call transcripts, is stored and transmitted securely. CCPA requires disclosure of how voice data is collected and whether it is shared with third parties. PII (Personally Identifiable Information) redaction is an optional feature that requires explicit configuration.
We hold SOC 2 Type II, ISO 27001, and HIPAA certifications and are GDPR compliant, all detailed at our Gladia compliance hub; the specific obligations each framework places on transcription deployments differ enough to warrant careful review of the applicable legal requirements before go-live.
"It's based in EU so it fits our GDPR compliance requirements... The team is very reactive and helpful" - Robin L. on G2
Driving ROI through transcription data
The most direct impact on cost-per-contact is agent wrap-up time. Accurate, auto-populated transcripts can reduce the manual data entry that extends post-call processing time, and what calls should capture for CRM and product analytics clarifies which named entities matter most for your workflow.
Aircall demonstrates this at scale. By integrating Gladia for post-call transcription and intelligence, Aircall cut transcription time by 95%, reducing per-call processing from 30 minutes to 1.5 minutes, and now runs more than 1 million calls per week through the API. That processing speed is what makes real-time CRM sync and same-day coaching feedback operationally viable.
Assessing transcription quality and reliability
Evaluating a transcription engine for contact center production requires testing it on the audio conditions it will actually face: phone-compressed audio, accented speech, multi-speaker calls, and mid-conversation language switches. Clean-audio demos prove nothing useful.
Accuracy, WER, and multilingual performance
Word Error Rate (WER) is a standard metric for measuring transcription accuracy, comparing the output to a human reference transcript. Higher error rates can disproportionately affect critical information like named entities, numbers, and product names, and a QA platform running sentiment analysis on a low-accuracy transcript may misclassify customer sentiment at rates that make automated scoring less reliable than manual review. Factors affecting transcript accuracy include phone compression, background noise, accent density, and vocabulary domain, all conditions your vendor must be tested on before you sign a contract.
We publish an open benchmark comparing Solaria-1 against 8 providers across 7 datasets and 74+ hours of audio using a fully reproducible methodology. Solaria-1 delivers on average 29% lower WER than alternatives on conversational speech. For a contact center scoring compliance disclosures, that gap directly determines whether you can trust automated QA output. A blind model accuracy comparison of 6 speech AI systems illustrates how dramatically production WER diverges from lab conditions.
European contact centers running English, French, German, Spanish, or Italian calls have a more targeted option: Solaria-3 is our most accurate model for real-world business audio, achieving 6.4% WER on Earnings22 (the only model under 7% on that dataset) and ranking #1 against AssemblyAI, ElevenLabs Scribe v2, Deepgram Nova-3, Speechmatics, and Mistral Voxtral on real customer recordings. Solaria-3 is async-only, which aligns directly with post-call QA and conversation intelligence workflows. For BPO deployments handling Southeast Asian or South Asian language pairs, Solaria-1 is the right choice: it covers a wider language breadth with native code-switching support, including languages no other API-level provider covers.
BPO environments compound this problem. Offshore sites in the Philippines, India, and Latin America produce calls with strong regional accents and frequent mid-call switches between English and a local language that standard models weren't tested on. Code-switching specifically breaks monolingual architectures: when a Manila-based agent moves from English to Tagalog mid-sentence, most APIs either return garbled output or silently fail on the Tagalog segment. Solaria-1 natively detects and processes code-switching across 100+ languages without requiring developers to pre-specify the language, covering languages including Tagalog, Bengali, Punjabi, Tamil, Urdu, and Marathi. For contact centers with BPO sites in Southeast Asia or South Asia, that language coverage is essential for usable QA data.
Real-time vs. async: choosing the right mode
You'll choose between two architecturally different transcription modes based entirely on your use case. Real-time streaming transcription processes audio as it arrives, delivering partial and final transcripts with low latency, and this mode fits live agent-assist tools where an AI system needs to suggest responses mid-call. We support real-time transcription with sub-300ms latency.
Async batch transcription processes a complete recording after the call ends, using full context to maximize accuracy, diarization quality, and multilingual handling. For post-call QA, CRM population, coaching scorecard generation, and conversation intelligence, async is the operationally correct choice because the latency cost is seconds or minutes (entirely acceptable), while the accuracy gain directly affects the reliability of every downstream system. Most modernizing contact center architectures rely on async pipelines for analytical workloads even when real-time is also in use for live-assist.
Speaker diarization for QA scoring
Speaker diarization answers the critical QA question: who said what. For a compliance scorecard, knowing whether the agent or the customer made a specific statement is non-negotiable, and a transcript that correctly captures every word but assigns them to the wrong speaker produces incorrect QA scores that can create legal liability.
Our diarization capability is available exclusively in async workflows because full-audio context enables better speaker attribution than streaming partial segments. The speaker diarization documentation covers configuration options for multi-party calls.
TCO for transcription software
Hidden per-feature pricing is the most common way contact center technology costs exceed budget projections. We bundle audio intelligence features, including diarization, translation, sentiment analysis, named entity recognition, summarization, and code-switching, into our base per-hour rate on Starter and Growth plans, making unit economics straightforward to model.
Our async rate starts at $0.61/hr on the Starter plan and drops to as low as $0.20/hr on Growth, with all audio intelligence features included at both tiers. For context: Deepgram's Nova-3 Multilingual streaming rate is approximately $0.35/hr pay-as-you-go as of May 2026 per their public pricing page, though the pre-recorded batch rate differs and should be verified directly for an apples-to-apples comparison with async post-call workflows, and additional features may be billed as add-ons.
AssemblyAI's advertised base rate can increase once speaker identification, sentiment, entity detection, and summarization are added. OpenAI's Whisper API (whisper-1) imposes a 25MB file cap and does not support streaming; their newer transcription endpoints (gpt-4o-transcribe) add streaming capability but remain distinct from a dedicated low-latency real-time pipeline.
On Growth and Enterprise plans, we never use customer audio for model training and no opt-out action is required. On the Starter plan, customer data can be used for model training by default. Confirm this distinction in your DPA review before signing.
Build vs. buy: when to use vendor transcription
The engineering case for self-hosting an open-source ASR model looks compelling until you account for total cost of ownership, but the hidden costs change the calculation significantly.
Operational risks of internal builds
Building and maintaining an in-house transcription pipeline ties up senior engineering capacity for months, increases infrastructure spend, and produces higher error rates in production compared to managed APIs. More critically, self-hosted setups can experience higher WER in production because they may lack the continuous model optimization and accent-specific fine-tuning that managed APIs invest in.
Evaluating vendor ROI against internal costs
Moving off a self-hosted setup to a managed API saves teams 20%+ in DevOps overhead, with most integrations reaching production in under a day. Integrations with common contact center telephony and orchestration platforms can remove the compatibility testing overhead that typically extends internal build timelines.
Common traps in transcription software sourcing
Verify accuracy using real call samples
Don't evaluate a transcription vendor using their provided demo audio or clean-read samples. Run your own POC using real calls from your BPO sites: compressed phone audio, accented speakers, multi-party calls, and any language switches your agents handle. Our async benchmark methodology is open and reproducible, meaning you can replicate the test using your own audio samples.
Hidden per-feature pricing
Ask every vendor for an "all-in price at 10,000 hours per month with diarization, sentiment, translation, and entity extraction enabled." The answer reveals the actual cost model quickly.
Spotting hidden data retention clauses
Some vendors enroll customers in model improvement programs by default, where customer audio is used to retrain models unless you submit an explicit opt-out request. For a contact center handling regulated customer data, that default is a compliance risk buried in the terms of service rather than disclosed upfront. Verify this by tier in your DPA review and confirm it applies from day one of the contract.
Performance degradation at scale
Some transcription APIs perform well in POC conditions at 5 to 10 concurrent calls but degrade under production load at higher concurrency. Before committing to a contract, verify the vendor has customers running at your expected production scale. Aircall processes over 1 million calls per week through our API without pre-provisioning.
Integration requirements for CCaaS and CRM systems
Syncing QA scores with CRM data
The standard post-call data flow runs as follows: the call ends, we transcribe the audio and extract entities, sentiment labels, and speaker-attributed segments, then the structured output is pushed via webhook to the CRM. QA scorecards are populated automatically based on rules applied to the transcript. The transcription API reference documents the full request schema, including word-level timestamps, speaker labels, entities, and sentiment scores, for teams building Salesforce, HubSpot, and CRM integrations on top of post-call transcription data.
Linking transcription to WFM workflows
Transcription data feeds WFM (Workforce Management) systems in two ways. Entity-level accuracy tracking per agent feeds coaching scorecards that supervisors use to target training interventions, and transcript-level sentiment signals per agent queue enable early detection of performance patterns before they affect service level.
Evaluation checklist for contact center transcription
Evaluating accuracy and latency needs
- Run a POC with real calls from your BPO sites, including accented audio and any multi-language pairs your agents use
- Measure WER specifically on named entities (account numbers, product names, agent names), not just average word accuracy
- Confirm whether the vendor's benchmark was tested on phone-compressed audio or studio recordings, and request the full methodology
- For live agent-assist workflows, confirm real-time latency under load at your expected peak concurrency
- For post-call QA and CRM, confirm async processing speed
Vendor data handling requirements
- Review security certifications and compliance documentation that apply to your deployment region
- Verify by pricing tier whether customer audio is used for model training by default
- Confirm data residency options available for your requirements
- Request the full Data Processing Agreement (DPA) before POC, not after contract signature
- Confirm PII redaction requires explicit configuration and is not assumed to be active by default
Essential vendor contract requirements
- Confirm 99.9%+ uptime SLA
- Verify support access model: direct engineering contact on Slack vs. ticket queue
- Confirm all-inclusive pricing for your required feature set at your expected volume
Key considerations for your transcription RFP
Transcription accuracy for global accents
- Provide WER results for English, Tagalog, Hindi, and Spanish on phone-compressed audio using a reproducible open methodology. Include the dataset and recording conditions.
- How does your model handle mid-call code-switching between English and Tagalog or English and Hindi? Provide a sample API response showing language detection metadata.
- What is your WER on named entity recognition specifically, separate from general word accuracy?
Deployment phases and timeframes
- What is the standard time from API key issuance to production-ready integration? Provide three customer references with their integration timelines.
- Which telephony platforms have native integrations with no custom middleware required?
- Do your engineers provide direct Slack access during integration, or does support route through a ticket system?
Does it support local data residency?
- In which cloud regions can audio and transcript data be stored? Can we select EU-west only for all processing and storage?
- List all certifications that apply to your EU deployment region: SOC 2 Type II, ISO 27001, HIPAA, GDPR, PCI.
Real-time vs. batch pricing models
- Provide an all-in price per hour at 1,000 and 10,000 hours per month with diarization, translation, sentiment, and entity extraction enabled.
- Are diarization, translation, and sentiment billed as add-ons, or are they included in the base per-hour rate at every plan tier?
Get started on our Starter plan and have your integration in production in less than a day, or test Gladia on your own multilingual audio to see how Solaria-1 handles accent-heavy speech and code-switching across 100+ languages.
FAQs
What is a standard word error rate (WER) for call center audio?
On Earnings22 financial call recordings, Solaria-3 achieves 6.4% WER, the only model under 7% in that dataset, and 33.9% on Switchboard, the only model under 35%; full methodology is at our open async benchmark. Teams running self-hosted setups report word error rates above 10% in production on noisy audio, which is the primary reason teams migrate to managed APIs.
Does Gladia support local data residency in Europe?
Yes. We're headquartered in France and operate clusters across the EU and US, supporting enterprise data residency requirements for both regions. Full certification details are at the Gladia compliance hub.
Is customer audio used to train Gladia's models?
On Growth and Enterprise plans, customer data is never used for model training by default and no opt-out action is required. On the Starter plan, customer data can be used for model training by default, so always verify this distinction in your Data Processing Agreement.
What is the latency of Gladia's real-time transcription?
Our real-time transcription pipeline delivers transcripts with sub-300ms latency, supporting live agent-assist workflows. Async transcription is the primary mode for post-call QA and analytics.
Is speaker diarization available for live, real-time calls?
No. High-accuracy speaker diarization is exclusively available in asynchronous batch workflows. For workflows that require both real-time transcription and speaker attribution, speaker attribution can be handled in post-processing for higher accuracy once the call completes.
Does Gladia charge extra for diarization, sentiment analysis, and entity detection?
No. On both Starter ($0.61/hr async) and Growth (as low as $0.20/hr async), diarization, sentiment analysis, named entity recognition, translation, summarization, and code-switching are all included in the base per-hour rate. Competitors with unbundled models advertise a lower base rate but charge separately for each of those features, so the effective per-hour cost rises significantly once you enable the full feature set. Ask any shortlisted vendor for an all-in price with diarization, sentiment, and entity extraction enabled before comparing rates.
Key terms glossary
Conversation intelligence: The automated analysis of customer interactions to extract structured insights, sentiment, and compliance scores from call transcripts.
Real-time transcription: The continuous, low-latency streaming of speech-to-text data during a live call, used to power live agent-assist tools.
Word Error Rate (WER): A standard metric for measuring transcription accuracy against a reference transcript, with accuracy typically degrading further on low-frequency terms like proper names and account numbers.
Diarization error rate (DER): A metric measuring errors in speaker attribution, which directly corrupts QA scores that depend on knowing whether the agent or the customer said a specific phrase.
Code-switching: The practice of alternating between languages during a conversation. Standard monolingual ASR models fail on code-switched audio, producing garbled output or silently dropping the switched-language segment.
Data residency: The contractual and technical requirement that audio data and transcripts are processed and stored within a specified geographic region, typically required for GDPR compliance in the EU.
