AI IVR: how speech-to-text powers conversational IVR

TL;DR: Conversational IVR replaces touch-tone menu trees with natural language input, but containment rates and FCR outcomes depend on multiple factors including STT accuracy, NLU engine performance, agent training, and access to customer information. When the real-time speech-to-text layer misreads accented speech or returns garbled transcripts, the NLU engine receives bad data and downstream routing decisions break. This article explains how real-time STT architecture works inside AI IVR, where the latency and accuracy thresholds sit, and how multilingual code-switching support prevents caller drop-off in BPO and global contact center environments.

AI call summaries for support and sales calls: automatic post-call recaps

TL;DR: Manual after-call work drives agent burnout, limits QA coverage to 1–3% of calls, and introduces data errors that corrupt every downstream system. AI call summaries solve the ACW problem, but only when the underlying transcription layer is accurate. A single transcription error, whether a misspelled name or a dropped compliance disclosure, corrupts your CRM, QA scorecards, and coaching workflows with data that was never said. This piece covers how to build that pipeline correctly: from selecting the right transcription model for your audio conditions and language mix, to routing structured outputs directly into CRM and QA systems without manual intervention, and what 100% call coverage looks like in production.

Automated call disposition with AI: codes, accuracy, and after-call time

TL;DR: Manual call dispositioning drives up Average Handle Time (AHT) and introduces human errors that corrupt customer experience analytics. By automating call tagging with AI, contact centers can reduce after-call work (ACW) significantly and classify 100% of calls consistently. Automated classification is only as accurate as the underlying transcript, however. We provide the high-precision, multilingual transcription layer beneath that classification step, delivering on average 29% lower WER than alternatives on conversational speech. That's the accuracy floor downstream AI classifiers need to assign the correct disposition code, even across accented and code-switching calls.

AI agent coaching in the contact center: turning call data into performance

TL;DR: Manual QA teams review fewer than 3% of contact center calls, leaving most agents without actionable feedback and creating conditions for high attrition. AI agent coaching closes that gap by automating scorecards, silence metrics, sentiment analysis, and compliance checks across 100% of interactions, but every downstream output is ceiling-bounded by transcription accuracy and speaker diarization quality. When the transcription layer is accurate, compliance flags reflect real agent behavior rather than speech-layer noise; when it isn't, scoring becomes a liability rather than a development tool.

What is conversation intelligence for telephony? How real-time transcription powers it

TL;DR: Conversation intelligence for telephony turns unstructured call audio into structured operational data: QA scorecards, CRM entries, coaching prompts, and compliance audit trails. Every output is capped by the accuracy of the transcription layer beneath it, which means WER on your actual call audio is the procurement metric that matters most. Most CI workflows run on async transcription, where full-context processing delivers higher accuracy at lower cost; real-time streaming applies to the narrower set of live-intervention use cases where the response must happen before the call ends.

Call recording compliance: GDPR, PCI DSS, and HIPAA for contact centers

TL;DR: Call recording compliance across GDPR, PCI DSS, and HIPAA involves more than a disclosure prompt at the start of each call. These frameworks impose specific rules on consent, redaction, EU data residency, and deletion timelines that manual processes consistently fail to meet at scale. Automated PII redaction at the transcription layer can protect cardholder data, PHI, and personally identifiable information across 100% of recorded interactions. Transcription accuracy sets the ceiling for entity detection: a missed word is a missed redaction, and missed redactions are compliance events. Evaluating WER on conversational speech (not clean studio audio) is the accuracy signal that matters most when selecting a transcription layer for compliance workflows.

Gladia CLI: transcribe audio from your terminal in one command

You have a recording on your desk and you need the text. Forty minutes later, you're reading API docs about polling intervals, writing an upload handler, and you still don't have the transcript. That gap between "I have audio" and "I have text” is filled with code nobody wants to write. Today we're shipping the shortcut.

Best speech-to-text APIs in 2026

Every speech-to-text vendor claims the lowest word error rate, the lowest latency, and the most transparent pricing. Run the same audio file through five providers and you'll get five different transcripts, five different bills, and at least two marketing pages that can't both be telling the whole story.

Call center transcription software: what enterprises should look for in 2026

TL;DR: Most contact centers evaluate transcription software using clean-audio lab benchmarks, then watch QA automation break down when BPO (Business Process Outsourcing) agents switch languages mid-call or phone-line noise degrades the signal. In 2026, the criteria that matter are real-world multilingual WER, all-inclusive per-hour pricing, and data sovereignty that holds up under GDPR and HIPAA audit. For enterprise teams, the highest-ROI evaluation step is testing on real BPO call samples rather than vendor demo audio, and asking every shortlisted provider for an all-in per-hour price with diarization, sentiment, and entity extraction enabled.