Switching your speech-to-text provider: A migration checklist for note-takers and contact center platforms

TL;DR: Switching your speech-to-text provider is a structural risk only if you skip the pre-migration audit. The real danger is not the cutover itself but continuing to run infrastructure that corrupts CRM entries, breaks LLM summaries, and inflates your per-hour cost with add-on fees you never modeled at scale. The four-stage phased cutover in this guide is designed to reach 100% production traffic without user-visible downtime, the same structural approach that let Aircall cut processing time by 95% and scale to over one million calls per week after adopting Gladia.

How does an AI note-taker improve meeting follow-ups?

TL;DR: After a meeting ends, someone has to write up the notes, assign action items, and draft the follow-up email, work that typically takes 3–8 minutes per session and compounds quickly across a team. AI note-takers eliminate that manual step by converting audio to structured follow-ups automatically, but scaling reliably requires choosing the right deployment pattern. Three architectures exist (platform-embedded models, standalone SaaS apps, and custom STT-LLM pipelines), each exposing a different set of infrastructure tradeoffs. The decision turns on three questions: how much control you need over the transcription model, what language coverage and data residency requirements your users impose, and what your unit economics look like at your actual meeting or call volume.

Best tools for automated call transcription and sentiment analysis

TL;DR: Choosing a call transcription API for conversation intelligence means evaluating the layer every downstream output depends on. Sentiment scoring, BANT extraction, talk ratio, and QA automation all inherit their accuracy from the STT layer that feeds them, which makes evaluating word error rate (WER) on noisy, multilingual audio the right starting point before choosing a CI stack. A single dropped negation can flip a sentiment score and propagate incorrect data through your CRM, pipeline reports, and coaching records. Higher transcription and diarization accuracy ultimately leads to more reliable insights and better business decisions.

Generate meeting notes automatically with Gladia and Make.com

TL;DR: Teams whose meeting workflows involve non-standard routing, regulated data handling, or tight integration with internal systems often can't configure off-the-shelf tools to fit. A purpose-built pipeline on Gladia gives you full control: decisions land in a structured log, action items reach the right person in Slack with consistent, speaker-attributed @mentions, and you define exactly where every byte of audio flows. Speaker diarization, powered by pyannoteAI, accurately identifies each speaker, so the same @mention lands on the same person every time. The result is a meeting workflow tuned to your stack, your compliance requirements, and your team's specific handoff patterns.

How decision intelligence improves customer service consistency in contact centers

TL;DR: Contact centers fail to deliver consistent service when routing infrastructure runs on static rules engines that cannot handle the complexity of real human conversation. Modern speech-to-text infrastructure addresses this by processing raw audio and feeding structured outputs to your CRM, using machine learning to analyze intent, sentiment, and speaker characteristics. Transcription accuracy sets the ceiling for every downstream action: a wrong word silently corrupts a CRM entry, a missed intent misfires a routing decision, and a misread sentiment score delays escalation. This playbook covers how to build and deploy that architecture without blowing your latency budget or your unit economics.

Real-time speech analytics for live agent assist

TL;DR: Live agent assist only works when the transcription layer delivers partial results fast enough for downstream NLP to process within a sub-second window. If the pipeline exceeds 1,000ms total, prompts arrive after agents have already spoken, which inflates Average Handle Time and erodes agent trust. This playbook covers the full real-time pipeline architecture, from streaming transcription through intent analysis to agent desktop rendering, and shows how contact centers can expand QA coverage from a 1-3% manual sample to 100% of interactions without adding headcount.

How to identify prospect companies from sales call transcripts

TL;DR: Most product teams try to run LLM extraction on raw, undiarized transcripts and end up with CRM records polluted by the sales rep's own company names, tools, and competitor mentions. The fix is an async-first pipeline that separates speaker dialogue before any entity extraction happens. This guide walks through a working Python and Claude API pipeline using our async transcription, pyannoteAI Precision-2 diarization, and Solaria-3 or Solaria-1 depending on your language mix, so you extract clean prospect-side signals and sync accurate data to your CRM.

Call center quality monitoring: how it works and where speech-to-text fits

TL;DR: Call center quality monitoring has evolved from supervisors spot-checking 1% to 5% of calls to AI systems that score every interaction automatically. The operational promise of 100% coverage is real, but it breaks down when the underlying transcription is inaccurate. Telephony audio is compressed, noisy, and often multilingual, and generic speech models weren't built for it. The transcript is the ceiling for every downstream scorecard, sentiment flag, and CRM entry, which makes the choice of speech-to-text infrastructure the most consequential decision in an automated QA build.