Build a lead scoring pipeline from sales call recordings with Gladia and Claude

TL;DR: Accurate hot/warm/cold lead scoring needs speaker-attributed transcripts. Without diarization, Claude cannot separate prospect buying signals from the sales rep's talk track, so any score is unreliable. Gladia's async API (Solaria-1) returns speaker-labeled, LLM-ready JSON, with diarization, sentiment, and named entity recognition included in the base per-hour rate on Starter and Growth plans, each enabled explicitly in the request. On Growth and Enterprise plans, audio is never used for model training with no opt-out required, keeping the pipeline safe for sensitive sales calls under GDPR and SOC 2 Type II.

How to flag low-confidence spans in AI meeting transcripts for reviewer QA

TL;DR: Transcription errors silently corrupt meeting summaries and CRM entries. Flag uncertainty with word-level confidence scores and pattern matching, then sync only the flagged spans to audio timestamps so reviewers verify the low-confidence parts instead of the whole transcript. Gladia's async API provides word-level confidence, pyannoteAI Precision-2 diarization, and native code-switching detection out of the box.

Mastering real-time transcription: speed, accuracy, and Gladia's AI advantage

TL;DR: Most use cases like meeting assistants, post-call analytics, and note-taking tools don't need real-time transcription. Async delivers higher accuracy and better speaker attribution because the model processes the complete recording. Sub-300ms latency is a functional requirement only for voice agents, live captions, and live agent assist tools where immediate output is non-negotiable. Gladia's Solaria-1 delivers around 270ms average latency with 100+ language support and native code-switching for the use cases that do require it.

Automated call scoring: Best practices for AI-powered QA and performance

TL;DR: Most contact centers manually review only a fraction of calls, leaving coaching decisions based on incomplete data. Automated call scoring closes that gap by combining async transcription with LLM-based evaluation, but every downstream score is bounded by the accuracy of your STT layer. When it fails on accented speakers or multilingual audio, compliance scores, sentiment flags, and coaching alerts all break, making STT engine selection the highest-leverage infrastructure decision in your QA stack.

Generate automated follow-up emails from meeting recordings with Gladia and Claude

TL;DR: The bottleneck in automated meeting follow-ups is not the LLM writing the email. It's the transcription layer feeding it: wrong speaker labels and missed entities produce emails that sound generic or silently corrupt your CRM. Building your own pipeline with Gladia and Claude gives you predictable per-hour billing and strict data controls on paid tiers, backed by Solaria-1's on average 29% lower WER than competing APIs on conversational speech.

Custom vocabulary for AI meeting note-takers: handling jargon, brand names, and technical terms

TL;DR: Injecting custom vocabulary at the ASR layer, not the LLM prompt layer, is the correct fix for entity errors in meeting transcripts. When the transcription layer gets a term wrong, every downstream system inherits the error, corrupting CRM entries, coaching scores, and summaries. Gladia's custom vocabulary feature covers named terms, phonetic variants, and language-scoped entries in a single API payload, included in the base price on Starter and Growth plans.

How to extract buyer intent and sales objections from calls using Gladia and Claude

TL;DR: Sales teams are sitting on recorded calls that could populate CRMs automatically, but the most common failure mode is the STT layer dropping words, misattributing speakers, or degrading silently on accented audio. Pairing Gladia's async transcription (Solaria-1) with Claude's strict JSON output mode fixes this, delivering full-context accuracy and diarization that streaming can't match, with on average 29% lower WER and 3x lower DER vs. alternatives so Claude receives a cleaner transcript and produces fewer false signals.

Power your sales: AI & speech-to-text for CRM data enrichment

TL;DR: If your STT API produces 10% WER on real sales calls, 10% of the lead data flowing into your CRM is wrong before your LLM ever touches it. Async batch transcription fixes this - full-context analysis of the complete recording produces better accuracy, speaker attribution, and multilingual handling than streaming. Gladia's Solaria-1 delivers on average 29% lower WER and 3x lower DER than alternatives across 74+ hours of conversational speech.

What is MCP in AI? Understanding the Model Context Protocol for audio

TL;DR: MCP gives AI models a uniform protocol to connect to external data sources, but transcription quality sets the ceiling on everything downstream - errors on accents, noise, or code-switching corrupt the context every agent reasons from. Gladia's Solaria-1 model delivers on average 29% lower WER and 3x lower DER than alternatives across 74+ hours of conversational speech, with full speaker attribution, 100+ language support, and true code-switching detection built in.