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Gladia integration recipes: connect calls to your CRM and workflow stack

TL;DR: Connecting call data to CRM and workflow tools requires accurate transcription at the base layer — downstream records are only as reliable as the words captured first. This guide covers four integration paths: Zapier for prototyping, Make.com for visual conditional routing, n8n self-hosted for high-volume privacy-sensitive workloads, and direct REST API for production infrastructure. Gladia's Solaria-1 model benchmarks at an average 29% lower WER and 3x lower DER versus alternatives.

Speech-To-Text

How to build a customer support call flow (AI blueprint)

TL;DR: Traditional IVR systems route calls by button press and fail when callers switch languages mid-sentence. AI-augmented flows treat audio as a structured pipeline: async transcription handles the high-accuracy layer for diarization, post-call summaries, and CRM sync, while real-time transcription at sub-300ms latency enables the live agent assist layer covered in this guide. Sub-300ms latency ensures guidance arrives while conversations progress; higher latency reduces assist usefulness. Building in-house involves substantial infrastructure, DevOps, and maintenance costs.

Speech-To-Text

Call transcription accuracy benchmarks: What contact centers should measure

TL;DR: Public STT benchmarks on clean English audio rarely predict how models perform on noisy, accented, multilingual contact center calls. To evaluate vendors properly, measure WER overall, WER per language and accent, DER, latency p50/p95/p99, and code-switching accuracy on your own production audio, not vendor test sets. Self-reported accuracy claims are meaningless without published methodology. Hidden per-feature fees for diarization and NER can compound significantly at scale compared to all-inclusive pricing models.

Ebook: Ultimate guide to using LLMs with speech recognition

Published on Jan 7, 2025

Large Language Models (LLMs) have enabled businesses to build advanced AI-driven features, but navigating the many available models and optimization techniques isn't always easy.

If you’re looking to combine speech recognition (STT) and LLMs for cutting-edge voice apps, look no further! Our ultimate guide is finally here, and it’s filled with valuable strategies and hands-on insights from our work with hundreds of audio-first companies and extensive interviews with experts in AI note-taking, sales enablement and customer support.

What you'll learn: