Most contact center platforms spend months fine-tuning their Large Language Model (LLM) prompts to generate better call summaries, yet they ignore the fact that their speech-to-text (STT) engine is feeding those LLMs corrupted transcripts. That is the core operational problem with how most customer experience (CX) teams deploy conversation intelligence today.
Conversation intelligence has evolved from a basic sales-coaching tool into a cross-functional operational engine. By converting unstructured phone calls into structured, searchable data, platforms can automate compliance audits, track customer sentiment, and guide agents in real time. The catch is that every automated workflow is only as reliable as the transcription layer it runs on. Real-time CI use cases (live agent assist cards triggered by detected objections, compliance alerts for regulatory disclosures, live translation for multilingual queues) require streaming transcription at ~300ms final latency, which magnifies the impact of codec compression, packet loss, and background noise on WER. Telephony audio is far messier than the clean studio recordings most speech-to-text vendors benchmark against.
What conversation intelligence is, and why it matters for CX teams
Conversation intelligence is the systematic identification, extraction, and analysis of signals from spoken communications. Unlike traditional call recording, which stores audio files for manual review, CI platforms interpret the content of conversations to surface trends, risks, and opportunities at scale. The two disciplines serve entirely different operational functions, so understanding that distinction is the starting point for building a CI stack that actually works.
Agent attrition is one of the highest operational costs in contact center environments, with each departure carrying recruiting, training, and lost-productivity overhead that compounds across high-volume teams. Automated CI-driven coaching is one of the proven levers that reduces this attrition by giving agents faster, more consistent performance feedback without depending on manual QA sampling. The ROI case is not theoretical, but it depends entirely on data quality at the source.
Why CI outperforms static recording
Call recording DVR for is passive storage: it gives you the raw audio but requires a human listener to extract any operational value. CI platforms take those recordings and run them through AI models to categorize intent, score compliance, identify objections, and populate CRM fields without manual effort. The difference is the gap between passive storage and active analysis.
Manual QA sampling covers only a fraction of call volume in most operations. Automated CI-driven analysis scores 100% of calls without adding QA headcount, catching compliance gaps and script deviations that manual review statistically cannot detect at scale. The business call transcript analysis workflows for sales and support are structurally similar but use different scoring criteria.
| Feature/metric |
Sales intelligence (e.g., Gong, Salesloft) |
CX intelligence (e.g., Contact Center as a Service platforms) |
| Primary focus |
Deal health, buyer signals, pipeline risk |
Operational efficiency, compliance, customer satisfaction |
| Key metrics |
Win rate, deal velocity, quota attainment |
First Call Resolution (FCR), Average Handle Time (AHT), Customer Satisfaction Score (CSAT) |
| Primary users |
Account executives, sales managers |
Support agents, QA evaluators, CX directors |
| Core workflow |
CRM opportunity updates, pipeline reviews |
Automated QA scoring, live agent coaching, compliance audits |
Why telephony audio is harder to transcribe
Video conference audio arrives at high bitrates with stable network conditions. Telephony is the opposite: compressed codecs (G.711, G.729) over SIP trunks introduce artifacts, packet loss creates gaps, and call center floors add background noise and overlapping speech. A speech model performing well on a studio recording can produce substantially higher WER on a live VoIP call from a Business Process Outsourcing (BPO) floor, and those errors compound through every downstream system. The factors affecting speech-to-text accuracy in telephony conditions are a prerequisite for setting realistic expectations in any CI deployment.
How AI identifies patterns in customer conversations
Modern CI systems run a multi-step pipeline to transform raw audio into structured operational data. Each extraction layer builds on the one before it, so errors introduced at transcription compound rather than cancel out as data flows downstream.
- Decoding customer intent at scale: Natural Language Processing (NLP) models classify the purpose of each call without manual tagging. A caller asking about an account balance, a cancellation, or a billing dispute triggers a different downstream routing path, coaching prompt, and QA rubric. Intent classification accuracy degrades directly with transcript quality: a misheard product name routes the summary to the wrong CRM field and produces a misleading QA tag. The technical walkthrough of AI contact center intent detection from transcribed audio shows this dependency on upstream accuracy explicitly throughout the pipeline.
- Quantifying customer emotion at scale: Two distinct technologies get conflated here, and the distinction matters for compliance and vendor evaluation.
- Text-based sentiment inference: NLP models analyze the transcript itself, categorizing utterances as positive, neutral, or negative based on word choice and phrase structure. This is what we provide, operating on the final transcript.
- Acoustic emotion detection: Models analyze vocal characteristics (tone, pace, stress) in the raw audio waveform, independent of the words spoken. We do not provide this capability.
Confirming which modality a vendor sells is a procurement step most teams skip. Our sentiment analysis documentation specifies the text-based approach clearly. - Capturing action items in real time: Commitment extraction identifies phrases where a speaker makes a promise or assigns follow-up. "I'll send the contract by Friday" becomes a structured action item routed to your CRM without the agent manually logging it, reducing Average Handle Time (AHT) and eliminating dropped follow-ups. Teams building these routing workflows can use our integration recipes to connect CRM and automation stack connections.
- Detecting call objections in real time: Real-time transcription (~300ms final latency) feeds a prompt engine that surfaces the relevant response card or escalation path before the agent's next sentence. This live agent assist use case targets both First Call Resolution (FCR) improvement and AHT reduction by giving agents structured guidance mid-call.
- Automating script compliance audits: Automated compliance auditing runs every transcript against a rule set, flags missing phrases (regulatory disclosures, payment scripts, escalation procedures), and produces audit trails that pass legal review. This pipeline connects transcription output to compliance workflows in production environments, as detailed on the CCaaS use case page.
Real-time vs. post-call: choosing your CI strategy
The choice between streaming and asynchronous transcription is an operational decision with direct impact on accuracy, cost, and the specific CI workflows you can run. Real-time CI use cases (live agent assist, compliance alerts, live translation) require streaming transcription because a post-call transcript doesn't exist yet when the agent needs it.
| Modality |
Latency |
Primary use cases |
Key technical advantage |
| Real-time (streaming) |
~300ms |
Live agent assist, live translation, real-time compliance alerts |
Immediate feedback loop during the active call |
| Asynchronous (batch) |
~60s per hour of audio |
Post-call QA, deep coaching, CRM logging, compliance auditing |
Higher accuracy, full-context diarization, lower cost |
Driving FCR through live agent prompts
Real-time transcription at ~300ms final latency enables agent assist cards to appear before the call ends. FCR improvement through live prompting requires the STT layer to stay accurate under telephony codec compression and background noise, because a misheard objection routes the wrong prompt to the agent. This is where accent robustness and code-switching support become direct FCR metrics.
Post-call QA and coaching workflows
For deep QA and coaching, async processing has a structural advantage: the model sees the full conversation before producing output. This context improves accuracy on domain vocabulary, enables full-conversation speaker diarization, and produces consistently better transcript quality than streaming. Our async api processes ~60 seconds per hour of audio, with speaker diarization powered by pyannoteAI's Precision-2 model available exclusively in async. For European contact-center and business audio specifically, Solaria-3 is our most accurate async model on noisy, fast-paced conversational recordings in English, French, German, Spanish, and Italian; Solaria-1 remains the right choice for the broader multilingual and code-switching workflows covered elsewhere in this article.
When to use real-time CI tools
Real-time CI is the right choice when the intervention must happen before the call ends. The specific use cases where this holds:
- Live agent assist cards triggered by detected objections or compliance-sensitive phrases
- Live translation for multilingual queues where agent and customer speak different languages
- Real-time compliance alerts for regulated industries where a disclosure must occur within a specific call window
For QA scoring, coaching reviews, CRM population, and churn prediction, async processing typically delivers higher accuracy at lower cost. It is the right modality when there is no mid-call action to take.
Why transcript quality determines AI accuracy
Most CI vendor evaluations skip this analysis. The WER at which your STT API operates in production, against your actual call audio with your actual agent accents, sets a hard ceiling on every downstream system. You cannot prompt-engineer your way out of a corrupted transcript.
How transcript errors break QA scoring
A single error in a product name, account number, or regulatory phrase does not affect the global WER score meaningfully, but it invalidates the entire automated QA check that depends on detecting that phrase. Entity errors cluster on domain-specific words, which are precisely the terms carrying the most downstream operational weight: names, product codes, account numbers, and compliance phrases. Information retrieval from transcripts remains workable at moderate WER, but named entity recognition degrades at lower error thresholds because those errors concentrate on exactly the terms automated scoring systems need.
Telephony-specific challenges: codecs, noise, and accents
Modern large-scale speech models were predominantly trained on higher-quality audio than G.729-compressed SIP calls from noisy BPO floors. The gap between benchmark accuracy on clean audio and production accuracy on telephony audio is where most CX operations encounter problems they did not anticipate during vendor evaluation. Multiple factors drive that gap: input audio quality, speaker traits including accents and code-switching, domain vocabulary deficits, and model training data diversity. Evaluating whether a vendor tested on conversational telephony audio rather than studio recordings is a critical procurement step; our async benchmark methodology tests exactly this, across 8 providers, 7 datasets, and 74+ hours of real conversational audio with open, reproducible methodology. We have also run an informal blind listening comparison where several models transcribe the same real audio side-by-side, a useful way to hear the difference in practice, not a substitute for the benchmark.
Transcription accuracy for global BPOs
Offshore BPO operations in the Philippines and India introduce two compounding challenges that break most English-optimized STT systems. First, regional accents produce WER degradation at code-switch points: a model that transcribes English accurately but degrades substantially on Tagalog utterances will show a blended score that looks acceptable until you check the failure rate on Filipino-language interactions. Second, code-switching, the natural mid-sentence alternation between two languages common in professional communication in Manila, Mumbai, and similar BPO markets, causes most speech APIs to treat language transitions as recognition errors rather than natural speech patterns.
Our Solaria-1 model supports 100+ languages including 42 that no other API-level STT provider covers, such as Tagalog, Bengali, Punjabi, Tamil, Urdu, Persian, and Marathi. True code-switching detection works across that full language set in both async and real-time modes, maintaining transcript accuracy through language transitions. Against alternatives tested in production conditions, Solaria-1 delivers on average 29% lower WERon conversational speech, validated across 7 datasets and 74+ hours of audio with open, reproducible benchmark methodology.
Production results confirm this at scale. Aircall cut transcription time by 95% (from 30 minutes to 1.5 minutes per call) and now processes over 1 million calls per week through Gladia. Claap achieved 1-3% WERi n production. A financial services customer running 800 concurrent sessions reported 98.5% numerical accuracy, which matters because a single digit error in an account number or payment amount invalidates any downstream automated action.
Integrating transcription with telephony infrastructure
Connecting a transcription API to existing telephony infrastructure requires understanding how audio travels from the caller through your stack before it reaches the speech model.
Connecting SIP trunks to CI pipelines
SIP trunks connect your IP Private Branch Exchange (PBX) to the Public Switched Telephone Network (PSTN) through a VoIP provider. The Session Border Controller (SBC) processes SIP signaling and media streams to defined destinations. For CI integration, the SBC forks the Real-time Transport Protocol (RTP) audio stream, sending one copy to the agent's endpoint and a second to your transcription API via WebSocket. This parallel audio routing enables CI processing while maintaining call quality.
Optimizing PBX data for transcription
Stereo call recording, where agent and customer audio are separated into distinct channels, produces dramatically better diarization accuracy than mixed mono recordings. When PBX metadata passes channel assignments to the transcription API, the diarization model starts with strong speaker identity priors rather than inferring separation from acoustic signatures alone. This reduces DER and prevents the QA failure mode where agent sentiment is misattributed to the customer. Diarization runs on pyannoteAI's Precision-2 model and is available exclusively in async workflows. Our speaker diarization documentation covers speaker count configuration and response structure.
Native voice-agent framework integrations
Modern voice agent orchestration frameworks handle session management, turn-taking logic, and downstream LLM routing. We integrate natively with LiveKit, Vapi, Pipecat, and Twilio, removing the middleware that adds latency and creates additional failure points.
Operational risks of custom CI development
The build-vs-buy decision for speech infrastructure looks deceptively attractive early on. An open-source model running on a GPU instance appears to offer full control without vendor dependency. The production reality is different.
Engineering cost of building transcription in-house
Self-hosting a speech model requires GPU infrastructure plus DevOps overhead for job queue management, worker scaling, retry logic, and failover. You need to provision capacity headroom for peak call volume, manage model updates when new weights are released, validate accuracy against your call audio after each update, and maintain the pipeline independently during incidents. Teams moving off self-hosted configurations to a managed API consistently report meaningful reductions in DevOps overhead, with most completing setup within a single working day.
Managing dialect variance in production
Maintaining a custom model that handles 100+ languages and regional dialects without a dedicated research team is not a viable path for most contact center operations. Achieving consistent accuracy across BPO languages like Tagalog, Tamil, or Urdu at scale is a research organization problem, not a DevOps problem.
Security standards for call data
For any contact center processing financial, health, or personally identifiable information, the compliance requirements for the transcription layer are non-negotiable. The evaluation checklist for any STT vendor in a regulated CX environment:
- SOC 2 Type II: Third-party audit confirming security controls operated effectively over a 6-to-12-month period, more rigorous than Type I
- ISO 27001: Information security management certification
- HIPAA: Required for any health-related interaction data
- GDPR: Required for any EU-resident caller data
- Data training policy: Whether customer audio trains models by default, and whether opt-out is a default or requires an enterprise contract clause
We hold SOC 2 Type II, ISO 27001, HIPAA, and GDPR certifications, documented at the compliance hub. On Growth and Enterprise plans, customer audio is never used for model training and no opt-out action is required. On the Starter plan, data can be used for training by default. The plan-level distinction matters during procurement: confirm which tier applies to your volume before the legal review. PII redaction must be explicitly enabled in the API configuration and is not on by default.
"It's based in EU so it fits our GDPR compliance requirements. The team is very reactive and helpful. The product works great. They're improving the product continuously." - Robin L on G2
Production timelines for CI software
Contact center platform Request for Proposal (RFP) cycles run 6 to 18 months. Custom speech pipelines run similar timelines when you account for model selection, infrastructure provisioning, accuracy validation, and compliance review. We connect via standard REST and WebSocket, with lightweight Python and JavaScript SDKs and native integrations for Twilio, LiveKit, Vapi, and Pipecat. Multiple customers report sub-24-hour integration times from first API call to production.
Pricing structure and infrastructure positioning
Deepgram and AssemblyAI price diarization, sentiment analysis, and entity extraction as add-ons separate from the base transcription rate, meaning the cost model you present to procurement at RFP initiation differs materially from the invoice at scale. Our Starter and Growth plans include all audio intelligence features, including diarization, translation, named entity recognition, sentiment analysis, and code-switching detection, in the base hourly rate: $0.61/hr async on Starter, as low as $0.20/hr on Growth. One price, one bill, against a cost model that does not change shape when you turn on features.
Get started with our Starter plan and have your integration in production in less than a day. Test Gladia on your own multilingual audio to see how it handles language detection, accent-heavy speech, and code-switching against your actual BPO call conditions.
FAQs
What is the difference between conversation intelligence and call analytics?
Call analytics tracks metadata like call duration, hold times, abandonment rates, and queue statistics, while conversation intelligence analyzes the actual spoken content of the call to extract intent, sentiment, compliance flags, and action items. The two are complementary but solve different operational problems.
How does CI connect to legacy telephony infrastructure?
Legacy telephony systems can typically connect to modern CI pipelines through integration methods such as SIP reinvites or Session Border Controllers that fork RTP audio streams to an external transcription API endpoint. In many cases, this integration approach allows connection without replacing existing telephony infrastructure.
What WER threshold matters for automated QA scoring?
Entity-dense QA scoring, which depends on detecting specific phrases and terms, degrades at WER levels well below the threshold where general intelligibility breaks down, because errors concentrate on the domain-specific vocabulary that automated scorecards depend on. Evaluating WER specifically on your call audio and vocabulary, rather than relying on general benchmark results, is the most reliable procurement test.
How does diarization error rate affect CI output?
DER measures the percentage of call time attributed to the wrong speaker, and errors above acceptable thresholds cause agent sentiment scores and compliance flags to be misattributed between agent and customer. Stereo channel recording passed correctly to the transcription layer can improve DER by giving the diarization model strong speaker identity priors rather than relying on acoustic inference alone.
How do automated QA systems handle regional dialects?
Automated QA systems handle regional dialects by routing audio through speech models trained on diverse, accented, conversational datasets. Solaria-1 covers 100+ languages, including 42 exclusive to Gladia, with native code-switching detection across all of them.
Does enabling PII redaction require a separate configuration?
Yes, PII redaction must be explicitly enabled in the API configuration and is not on by default in any Gladia plan. Teams in regulated industries should configure this at the integration level before processing live call audio. For the specific configuration steps, the AI transcription legal and safety guide covers the compliance requirements in detail.
Key terms glossary
Word Error Rate (WER): The standard metric for measuring speech-to-text accuracy, calculated by dividing the sum of insertions, deletions, and substitutions by the total number of words spoken. Lower WER means higher accuracy, but entity-dense workflows are sensitive to errors at rates that look acceptable on aggregate WER scores.
Diarization Error Rate (DER): The metric evaluating speaker diarization performance, measuring the percentage of call time attributed to the wrong speaker. The acceptable DER threshold varies by use case; automated QA workflows generally require low enough DER that agent and customer utterances are reliably distinguished, with requirements tightening when sentiment scores and compliance flags are attributed per speaker.
Code-switching: The practice of alternating between two or more languages or dialects within a single conversation, common in multilingual BPO environments. Native code-switching support maintains transcript accuracy through language transitions rather than treating them as recognition errors.
SIP Trunking: A technology that streams voice calls over an internet connection rather than a traditional physical phone line. SIP trunks connect IP PBX systems to the PSTN through VoIP providers and are the primary audio transport layer in modern contact centers.
PII redaction: An optional feature that identifies and masks personally identifiable information (names, phone numbers, account numbers) in transcripts. Must be explicitly enabled in API configuration and is not active by default on any Gladia plan.
First Call Resolution (FCR): The percentage of customer issues resolved on the first contact without requiring follow-up calls or escalations. Higher FCR reduces operational costs and improves customer satisfaction.
Average Handle Time (AHT): The average duration of a customer interaction, including talk time, hold time, and after-call work. AHT is a key efficiency metric in contact centers but must be balanced against quality.
Customer Satisfaction Score (CSAT): A metric that measures customer satisfaction with a specific interaction or service, typically collected through post-call surveys. CSAT scores directly inform CX strategy and agent coaching priorities.
